id,category,question,vega_code,train_code
1,spatial_aggregation,Plot the top 10 states by average PM2.5 in 2020 as a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2020]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nlargest(10, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 10 States by Average PM2.5 in 2020', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2020]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nlargest(10, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 10 States by Average PM2.5 in 2020', width=500, height=300)
     return chart


</code>
"
2,spatial_aggregation,"Show the top 10 states by average PM10 in 2019 as a bar chart, sorted in descending order.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2019]
    df = df.groupby('state')['PM10'].mean().reset_index().dropna()
    df = df.nlargest(10, 'PM10')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Top 10 States by Average PM10 in 2019', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2019]
     df = df.groupby('state')['PM10'].mean().reset_index().dropna()
     df = df.nlargest(10, 'PM10')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Top 10 States by Average PM10 in 2019', width=500, height=300)
     return chart


</code>
"
3,spatial_aggregation,Visualize the bottom 10 states with the lowest average PM2.5 in 2021 using a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2021]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nsmallest(10, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Bottom 10 States by Average PM2.5 in 2021', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2021]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nsmallest(10, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Bottom 10 States by Average PM2.5 in 2021', width=500, height=300)
     return chart


</code>
"
4,spatial_aggregation,Plot the average PM2.5 across all states in February 2018 as a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 2)]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('PM2.5', ascending=False)
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='plasma'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Average PM2.5 by State – February 2018', width=500, height=400)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month == 2)]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('PM2.5', ascending=False)
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='plasma'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Average PM2.5 by State – February 2018', width=500, height=400)
     return chart


</code>
"
5,spatial_aggregation,Show a bar chart of the top 10 cities by median PM2.5 in 2022.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2022]
    df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
    df = df.nlargest(10, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
        y=alt.Y('city:N', sort='-x', title='City'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
        tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 10 Cities by Median PM2.5 in 2022', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2022]
     df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
     df = df.nlargest(10, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
         y=alt.Y('city:N', sort='-x', title='City'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
         tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 10 Cities by Median PM2.5 in 2022', width=500, height=300)
     return chart


</code>
"
6,spatial_aggregation,Plot the median PM10 for each state in summer 2020 (April–June) as a sorted bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['Timestamp'].dt.year == 2020) & (data['Timestamp'].dt.month.isin([4,5,6]))]
    df = df.groupby('state')['PM10'].median().reset_index().dropna()
    df = df.sort_values('PM10', ascending=False)
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM10:Q', title='Median PM10 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM10:Q', scale=alt.Scale(scheme='yelloworangered'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Median PM10 by State – Summer 2020 (Apr–Jun)', width=500, height=400)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['Timestamp'].dt.year == 2020) & (data['Timestamp'].dt.month.isin([4,5,6]))]
     df = df.groupby('state')['PM10'].median().reset_index().dropna()
     df = df.sort_values('PM10', ascending=False)
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM10:Q', title='Median PM10 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM10:Q', scale=alt.Scale(scheme='yelloworangered'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Median PM10 by State – Summer 2020 (Apr–Jun)', width=500, height=400)
     return chart


</code>
"
7,spatial_aggregation,Show a bar chart comparing the 75th percentile PM2.5 of all states in winter 2021 (November–February).,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.month.isin([11,12,1,2])]
    df = df[df['Timestamp'].dt.year.isin([2021,2022])]
    df = df.groupby('state')['PM2.5'].quantile(0.75).reset_index().dropna()
    df.columns = ['state','PM2.5_p75']
    df = df.sort_values('PM2.5_p75', ascending=False)
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5_p75:Q', title='75th Percentile PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM2\.5_p75:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5_p75:Q', format='.1f', title='P75 PM2.5')]
    ).properties(title='75th Percentile PM2.5 by State – Winter 2021', width=500, height=400)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.month.isin([11,12,1,2])]
     df = df[df['Timestamp'].dt.year.isin([2021,2022])]
     df = df.groupby('state')['PM2.5'].quantile(0.75).reset_index().dropna()
     df.columns = ['state','PM2.5_p75']
     df = df.sort_values('PM2.5_p75', ascending=False)
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5_p75:Q', title='75th Percentile PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM2\.5_p75:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5_p75:Q', format='.1f', title='P75 PM2.5')]
     ).properties(title='75th Percentile PM2.5 by State – Winter 2021', width=500, height=400)
     return chart


</code>
"
8,temporal_aggregation,Plot the monthly average PM2.5 trend for Delhi from 2017 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Delhi'].copy()
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='steelblue').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly Average PM2.5 Trend – Delhi (2017–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Delhi'].copy()
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='steelblue').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly Average PM2.5 Trend – Delhi (2017–2024)', width=600, height=300)
     return chart


</code>
"
9,temporal_aggregation,Show the monthly average PM10 trend for Mumbai from 2018 to 2023 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Mumbai'].copy()
    df = df[(df['Timestamp'].dt.year >= 2018) & (df['Timestamp'].dt.year <= 2023)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Mumbai (2018–2023)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Mumbai'].copy()
     df = df[(df['Timestamp'].dt.year >= 2018) & (df['Timestamp'].dt.year <= 2023)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Mumbai (2018–2023)', width=600, height=300)
     return chart


</code>
"
10,temporal_aggregation,Plot the yearly average PM2.5 for India (all stations) from 2017 to 2024 as a line chart with points.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data.groupby(data['Timestamp'].dt.year)['PM2.5'].mean().reset_index().dropna()
    df.columns = ['Year','PM2.5']
    df['Year'] = df['Year'].astype(str)
    line = alt.Chart(df).mark_line(color='firebrick').encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    points = alt.Chart(df).mark_point(filled=True, color='firebrick', size=60).encode(
        x='Year:O', y='PM2\.5:Q',
        tooltip=['Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (line + points).properties(title='Yearly Average PM2.5 – India (2017–2024)', width=500, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data.groupby(data['Timestamp'].dt.year)['PM2.5'].mean().reset_index().dropna()
     df.columns = ['Year','PM2.5']
     df['Year'] = df['Year'].astype(str)
     line = alt.Chart(df).mark_line(color='firebrick').encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     points = alt.Chart(df).mark_point(filled=True, color='firebrick', size=60).encode(
         x='Year:O', y='PM2\.5:Q',
         tooltip=['Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (line + points).properties(title='Yearly Average PM2.5 – India (2017–2024)', width=500, height=300)


</code>
"
11,temporal_aggregation,Show a monthly bar chart of the number of days Delhi exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2019.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Delhi') & (data['Timestamp'].dt.year == 2019)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Delhi Exceeded WHO PM2.5 Guideline per Month – 2019', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Delhi') & (data['Timestamp'].dt.year == 2019)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Delhi Exceeded WHO PM2.5 Guideline per Month – 2019', width=500, height=300)
     return chart


</code>
"
12,temporal_aggregation,"Plot the average PM2.5 for each season (Winter, Summer, Monsoon, Post-Monsoon) across all years as a bar chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    def season(m):
        if m in [12,1,2]: return 'Winter'
        elif m in [3,4,5]: return 'Summer'
        elif m in [6,7,8,9]: return 'Monsoon'
        else: return 'Post-Monsoon'
    df = data.copy()
    df['Season'] = df['Timestamp'].dt.month.apply(season)
    df = df.groupby('Season')['PM2.5'].mean().reset_index().dropna()
    order = ['Winter','Summer','Post-Monsoon','Monsoon']
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Season:N', sort=order, title='Season'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('Season:N', scale=alt.Scale(scheme='category10'), legend=None),
        tooltip=['Season:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Average PM2.5 by Season (All Years)', width=400, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     def season(m):
         if m in [12,1,2]: return 'Winter'
         elif m in [3,4,5]: return 'Summer'
         elif m in [6,7,8,9]: return 'Monsoon'
         else: return 'Post-Monsoon'
     df = data.copy()
     df['Season'] = df['Timestamp'].dt.month.apply(season)
     df = df.groupby('Season')['PM2.5'].mean().reset_index().dropna()
     order = ['Winter','Summer','Post-Monsoon','Monsoon']
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Season:N', sort=order, title='Season'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('Season:N', scale=alt.Scale(scheme='category10'), legend=None),
         tooltip=['Season:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Average PM2.5 by Season (All Years)', width=400, height=300)
     return chart


</code>
"
13,temporal_aggregation,Show the monthly average PM2.5 for Kolkata in 2021 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Kolkata') & (data['Timestamp'].dt.year == 2021)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Kolkata 2021', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Kolkata') & (data['Timestamp'].dt.year == 2021)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Kolkata 2021', width=450, height=280)


</code>
"
14,temporal_aggregation,Plot the weekly average PM2.5 for Bengaluru in 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Bengaluru') & (data['Timestamp'].dt.year == 2022)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Bengaluru 2022', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Bengaluru') & (data['Timestamp'].dt.year == 2022)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Bengaluru 2022', width=600, height=300)
     return chart


</code>
"
15,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Delhi, Maharashtra, and Kerala from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Delhi', 'Maharashtra', 'Kerala']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Delhi vs Maharashtra vs Kerala', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Delhi', 'Maharashtra', 'Kerala']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Delhi vs Maharashtra vs Kerala', width=550, height=320)
     return chart


</code>
"
16,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Punjab, Haryana, and Uttar Pradesh in 2020 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Punjab', 'Haryana', 'Uttar Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2020)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Punjab, Haryana, UP – 2020', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Punjab', 'Haryana', 'Uttar Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2020)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Punjab, Haryana, UP – 2020', width=550, height=320)
     return chart


</code>
"
17,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Mumbai, Delhi, and Bengaluru in 2021 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Mumbai', 'Delhi', 'Bengaluru']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2021)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Mumbai vs Delhi vs Bengaluru – 2021', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Mumbai', 'Delhi', 'Bengaluru']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2021)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Mumbai vs Delhi vs Bengaluru – 2021', width=550, height=320)
     return chart


</code>
"
18,spatio_temporal_aggregation,Plot the yearly average PM2.5 for the top 5 most polluted states from 2017 to 2024 as a multi-line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    top5 = data.groupby('state')['PM2.5'].mean().nlargest(5).index.tolist()
    df = data[data['state'].isin(top5)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends – Top 5 Most Polluted States', width=600, height=350)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     top5 = data.groupby('state')['PM2.5'].mean().nlargest(5).index.tolist()
     df = data[data['state'].isin(top5)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends – Top 5 Most Polluted States', width=600, height=350)
     return chart


</code>
"
19,spatio_temporal_aggregation,Show the monthly average PM2.5 for Delhi across each year from 2017 to 2024 as small multiples (faceted by year).,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Delhi'].copy()
    df['Year'] = df['Timestamp'].dt.year
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='steelblue').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5'),
        tooltip=['Year:O','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).facet(
        facet=alt.Facet('Year:O', title='Year'),
        columns=4
    ).properties(title='Monthly PM2.5 in Delhi by Year (2017–2024)')
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Delhi'].copy()
     df['Year'] = df['Timestamp'].dt.year
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='steelblue').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5'),
         tooltip=['Year:O','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).facet(
         facet=alt.Facet('Year:O', title='Year'),
         columns=4
     ).properties(title='Monthly PM2.5 in Delhi by Year (2017–2024)')
     return chart


</code>
"
20,spatio_temporal_aggregation,"Create a faceted bar chart showing top 5 states by average PM2.5 per year for 2018, 2019, 2020, and 2021.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year.isin([2018,2019,2020,2021])].copy()
    df['Year'] = df['Timestamp'].dt.year
    agg = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    top = agg.groupby('Year').apply(lambda x: x.nlargest(5,'PM2.5')).reset_index(drop=True)
    chart = alt.Chart(top).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Avg PM2.5'),
        y=alt.Y('state:N', sort='-x'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).facet(facet='Year:O', columns=2).properties(title='Top 5 States by PM2.5 per Year')
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year.isin([2018,2019,2020,2021])].copy()
     df['Year'] = df['Timestamp'].dt.year
     agg = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     top = agg.groupby('Year').apply(lambda x: x.nlargest(5,'PM2.5')).reset_index(drop=True)
     chart = alt.Chart(top).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Avg PM2.5'),
         y=alt.Y('state:N', sort='-x'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).facet(facet='Year:O', columns=2).properties(title='Top 5 States by PM2.5 per Year')
     return chart


</code>
"
21,spatio_temporal_aggregation,Create a heatmap showing the average PM2.5 by month (x-axis) and year (y-axis) for Delhi.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Delhi'].copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_rect().encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Year:N', title='Year'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='Avg PM2.5'),
        tooltip=['Year:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Heatmap – Delhi (Month × Year)', width=500, height=280)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Delhi'].copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_rect().encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Year:N', title='Year'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='Avg PM2.5'),
         tooltip=['Year:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Heatmap – Delhi (Month × Year)', width=500, height=280)
     return chart


</code>
"
22,spatio_temporal_aggregation,Plot a heatmap of average PM10 by state (y-axis) and month (x-axis) for 2021.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2021].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['state','Month'])['PM10'].mean().reset_index().dropna()
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_rect().encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('state:N', title='State'),
        color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), title='Avg PM10'),
        tooltip=['state:N','MonthName:N', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='PM10 Heatmap by State and Month – 2021', width=500, height=400)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2021].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['state','Month'])['PM10'].mean().reset_index().dropna()
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_rect().encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('state:N', title='State'),
         color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), title='Avg PM10'),
         tooltip=['state:N','MonthName:N', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='PM10 Heatmap by State and Month – 2021', width=500, height=400)
     return chart


</code>
"
23,spatio_temporal_aggregation,Create a heatmap showing the average PM2.5 by state and year from 2017 to 2024.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_rect().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('state:N', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='plasma'), title='Avg PM2.5'),
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Average PM2.5 Heatmap by State and Year', width=500, height=400)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_rect().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('state:N', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='plasma'), title='Avg PM2.5'),
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Average PM2.5 Heatmap by State and Year', width=500, height=400)
     return chart


</code>
"
24,spatio_temporal_aggregation,Show a heatmap of average PM2.5 for the top 8 most polluted states by month for 2020.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    top8 = data.groupby('state')['PM2.5'].mean().nlargest(8).index.tolist()
    df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2020)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_rect().encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('state:N', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
        tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Heatmap – Top 8 Polluted States by Month (2020)', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     top8 = data.groupby('state')['PM2.5'].mean().nlargest(8).index.tolist()
     df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2020)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_rect().encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('state:N', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
         tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Heatmap – Top 8 Polluted States by Month (2020)', width=500, height=300)
     return chart


</code>
"
25,spatial_aggregation,"Create a scatter plot of PM2.5 vs PM10 for all stations in 2020, colored by state.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2020]
    df = df.groupby(['station','state'])[['PM2.5','PM10']].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_point(opacity=0.6, size=60).encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['station:N','state:N',
                 alt.Tooltip('PM2\.5:Q', format='.1f'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='PM2.5 vs PM10 by Station – 2020', width=500, height=400)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2020]
     df = df.groupby(['station','state'])[['PM2.5','PM10']].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_point(opacity=0.6, size=60).encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['station:N','state:N',
                  alt.Tooltip('PM2\.5:Q', format='.1f'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='PM2.5 vs PM10 by Station – 2020', width=500, height=400)
     return chart


</code>
"
26,population_based,"Plot a scatter chart of state-level average PM2.5 versus population for 2020, with point size representing area.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    pm = data[data['Timestamp'].dt.year == 2020].groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = pm.merge(states_data, on='state')
    chart = alt.Chart(df).mark_point(filled=True, opacity=0.7).encode(
        x=alt.X('population:Q', title='Population', axis=alt.Axis(format='~s')),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        size=alt.Size('area (km2):Q', title='Area (km²)', scale=alt.Scale(range=[50,1000])),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f'),
                 alt.Tooltip('population:Q', format=','), 'area (km2):Q']
    ).properties(title='PM2.5 vs Population (size=Area) – 2020', width=500, height=400)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     pm = data[data['Timestamp'].dt.year == 2020].groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = pm.merge(states_data, on='state')
     chart = alt.Chart(df).mark_point(filled=True, opacity=0.7).encode(
         x=alt.X('population:Q', title='Population', axis=alt.Axis(format='~s')),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         size=alt.Size('area (km2):Q', title='Area (km²)', scale=alt.Scale(range=[50,1000])),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f'),
                  alt.Tooltip('population:Q', format=','), 'area (km2):Q']
     ).properties(title='PM2.5 vs Population (size=Area) – 2020', width=500, height=400)
     return chart


</code>
"
27,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Delhi stations in 2022, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Delhi') & (data['Timestamp'].dt.year == 2022)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Delhi Stations 2022', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Delhi') & (data['Timestamp'].dt.year == 2022)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Delhi Stations 2022', width=450, height=350)


</code>
"
28,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Delhi, Maharashtra, and Karnataka across 2018, 2019, 2020, and 2021.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Delhi', 'Maharashtra', 'Karnataka']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2018,2019,2020,2021]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2018–2021)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Delhi', 'Maharashtra', 'Karnataka']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2018,2019,2020,2021]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2018–2021)', width=500, height=320)
     return chart


</code>
"
29,spatio_temporal_aggregation,Plot a grouped bar chart of average PM10 by season and year (2019–2022) for all of India.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    def season(m):
        if m in [12,1,2]: return 'Winter'
        elif m in [3,4,5]: return 'Summer'
        elif m in [6,7,8,9]: return 'Monsoon'
        else: return 'Post-Monsoon'
    df = data[data['Timestamp'].dt.year.isin([2019,2020,2021,2022])].copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df['Season'] = df['Timestamp'].dt.month.apply(season)
    df = df.groupby(['Season','Year'])['PM10'].mean().reset_index().dropna()
    order = ['Winter','Summer','Post-Monsoon','Monsoon']
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Season:N', sort=order, title='Season'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('Year:N', title='Year'),
        xOffset='Year:N',
        tooltip=['Season:N','Year:N', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Average PM10 by Season and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     def season(m):
         if m in [12,1,2]: return 'Winter'
         elif m in [3,4,5]: return 'Summer'
         elif m in [6,7,8,9]: return 'Monsoon'
         else: return 'Post-Monsoon'
     df = data[data['Timestamp'].dt.year.isin([2019,2020,2021,2022])].copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df['Season'] = df['Timestamp'].dt.month.apply(season)
     df = df.groupby(['Season','Year'])['PM10'].mean().reset_index().dropna()
     order = ['Winter','Summer','Post-Monsoon','Monsoon']
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Season:N', sort=order, title='Season'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('Year:N', title='Year'),
         xOffset='Year:N',
         tooltip=['Season:N','Year:N', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Average PM10 by Season and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
30,spatio_temporal_aggregation,Create a grouped bar chart comparing the average PM2.5 in Winter vs Summer for the top 6 most polluted states.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    top6 = data.groupby('state')['PM2.5'].mean().nlargest(6).index.tolist()
    df = data[data['state'].isin(top6)].copy()
    df['Season'] = df['Timestamp'].dt.month.apply(
        lambda m: 'Winter' if m in [12,1,2] else ('Summer' if m in [3,4,5] else None))
    df = df.dropna(subset=['Season'])
    df = df.groupby(['state','Season'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('state:N', title='State'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('Season:N', title='Season'),
        xOffset='Season:N',
        tooltip=['state:N','Season:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Winter vs Summer PM2.5 – Top 6 Polluted States', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     top6 = data.groupby('state')['PM2.5'].mean().nlargest(6).index.tolist()
     df = data[data['state'].isin(top6)].copy()
     df['Season'] = df['Timestamp'].dt.month.apply(
         lambda m: 'Winter' if m in [12,1,2] else ('Summer' if m in [3,4,5] else None))
     df = df.dropna(subset=['Season'])
     df = df.groupby(['state','Season'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('state:N', title='State'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('Season:N', title='Season'),
         xOffset='Season:N',
         tooltip=['state:N','Season:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Winter vs Summer PM2.5 – Top 6 Polluted States', width=550, height=320)
     return chart


</code>
"
31,funding_based,"Show a stacked bar chart of total NCAP funding per state broken down by fiscal year (FY19-20, FY20-21, FY21-22).","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = ncap_funding_data.groupby('state')[
        ['Amount released during FY 2019-20',
         'Amount released during FY 2020-21',
         'Amount released during FY 2021-22']
    ].sum().reset_index()
    df = df.melt(id_vars='state', var_name='FY', value_name='Amount (Cr)')
    df['FY'] = df['FY'].str.replace('Amount released during ', '')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Amount (Cr):Q', title='Amount Released (Cr)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('FY:N', title='Fiscal Year'),
        tooltip=['state:N','FY:N', alt.Tooltip('Amount (Cr):Q', format='.1f')]
    ).properties(title='NCAP Funding by State and Fiscal Year', width=500, height=350)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = ncap_funding_data.groupby('state')[
         ['Amount released during FY 2019-20',
          'Amount released during FY 2020-21',
          'Amount released during FY 2021-22']
     ].sum().reset_index()
     df = df.melt(id_vars='state', var_name='FY', value_name='Amount (Cr)')
     df['FY'] = df['FY'].str.replace('Amount released during ', '')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Amount (Cr):Q', title='Amount Released (Cr)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('FY:N', title='Fiscal Year'),
         tooltip=['state:N','FY:N', alt.Tooltip('Amount (Cr):Q', format='.1f')]
     ).properties(title='NCAP Funding by State and Fiscal Year', width=500, height=350)
     return chart


</code>
"
32,funding_based,Plot total NCAP funding vs total fund utilisation for each state as a grouped bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = ncap_funding_data.groupby('state')[
        ['Total fund released','Utilisation as on June 2022']].sum().reset_index()
    df = df.melt(id_vars='state', var_name='Type', value_name='Amount (Cr)')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('state:N', title='State'),
        y=alt.Y('Amount (Cr):Q', title='Amount (Crores)'),
        color=alt.Color('Type:N', title=''),
        xOffset='Type:N',
        tooltip=['state:N','Type:N', alt.Tooltip('Amount (Cr):Q', format='.1f')]
    ).properties(title='NCAP Funding Released vs Utilised by State', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = ncap_funding_data.groupby('state')[
         ['Total fund released','Utilisation as on June 2022']].sum().reset_index()
     df = df.melt(id_vars='state', var_name='Type', value_name='Amount (Cr)')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('state:N', title='State'),
         y=alt.Y('Amount (Cr):Q', title='Amount (Crores)'),
         color=alt.Color('Type:N', title=''),
         xOffset='Type:N',
         tooltip=['state:N','Type:N', alt.Tooltip('Amount (Cr):Q', format='.1f')]
     ).properties(title='NCAP Funding Released vs Utilised by State', width=550, height=320)
     return chart


</code>
"
33,funding_based,Create a bar chart of NCAP funding utilisation rate (%) for each state.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = ncap_funding_data.groupby('state')[
        ['Total fund released','Utilisation as on June 2022']].sum().reset_index()
    df['Utilisation Rate (%)'] = (df['Utilisation as on June 2022'] / df['Total fund released'] * 100).round(1)
    df = df.sort_values('Utilisation Rate (%)', ascending=False)
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Utilisation Rate (%):Q', title='Utilisation Rate (%)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('Utilisation Rate (%):Q', scale=alt.Scale(scheme='greens'), legend=None),
        tooltip=['state:N', alt.Tooltip('Utilisation Rate (%):Q', format='.1f')]
    ).properties(title='NCAP Fund Utilisation Rate by State', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = ncap_funding_data.groupby('state')[
         ['Total fund released','Utilisation as on June 2022']].sum().reset_index()
     df['Utilisation Rate (%)'] = (df['Utilisation as on June 2022'] / df['Total fund released'] * 100).round(1)
     df = df.sort_values('Utilisation Rate (%)', ascending=False)
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Utilisation Rate (%):Q', title='Utilisation Rate (%)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('Utilisation Rate (%):Q', scale=alt.Scale(scheme='greens'), legend=None),
         tooltip=['state:N', alt.Tooltip('Utilisation Rate (%):Q', format='.1f')]
     ).properties(title='NCAP Fund Utilisation Rate by State', width=500, height=320)
     return chart


</code>
"
34,funding_based,Show a scatter plot of total NCAP funding vs average PM2.5 (2020) per state.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    pm = data[data['Timestamp'].dt.year == 2020].groupby('state')['PM2.5'].mean().reset_index()
    funding = ncap_funding_data.groupby('state')['Total fund released'].sum().reset_index()
    df = pm.merge(funding, on='state').dropna()
    chart = alt.Chart(df).mark_point(filled=True, size=100).encode(
        x=alt.X('Total fund released:Q', title='Total NCAP Funding (Cr)'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 – 2020 (µg/m³)'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f'),
                 alt.Tooltip('Total fund released:Q', format='.1f')]
    ).properties(title='NCAP Funding vs Average PM2.5 by State (2020)', width=450, height=350)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     pm = data[data['Timestamp'].dt.year == 2020].groupby('state')['PM2.5'].mean().reset_index()
     funding = ncap_funding_data.groupby('state')['Total fund released'].sum().reset_index()
     df = pm.merge(funding, on='state').dropna()
     chart = alt.Chart(df).mark_point(filled=True, size=100).encode(
         x=alt.X('Total fund released:Q', title='Total NCAP Funding (Cr)'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 – 2020 (µg/m³)'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f'),
                  alt.Tooltip('Total fund released:Q', format='.1f')]
     ).properties(title='NCAP Funding vs Average PM2.5 by State (2020)', width=450, height=350)
     return chart


</code>
"
35,funding_based,Visualize NCAP city-level funding for FY 2021-22 as a horizontal bar chart for the top 15 cities.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = ncap_funding_data[['city','state','Amount released during FY 2021-22']].copy()
    df.columns = ['city','state','Amount (Cr)']
    df = df.nlargest(15, 'Amount (Cr)')
    df['City_State'] = df['city'] + ', ' + df['state']
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Amount (Cr):Q', title='Amount Released (Cr)'),
        y=alt.Y('City_State:N', sort='-x', title='City, State'),
        color=alt.Color('state:N', title='State'),
        tooltip=['city:N','state:N', alt.Tooltip('Amount (Cr):Q', format='.1f')]
    ).properties(title='Top 15 Cities by NCAP Funding – FY 2021-22', width=500, height=400)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = ncap_funding_data[['city','state','Amount released during FY 2021-22']].copy()
     df.columns = ['city','state','Amount (Cr)']
     df = df.nlargest(15, 'Amount (Cr)')
     df['City_State'] = df['city'] + ', ' + df['state']
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Amount (Cr):Q', title='Amount Released (Cr)'),
         y=alt.Y('City_State:N', sort='-x', title='City, State'),
         color=alt.Color('state:N', title='State'),
         tooltip=['city:N','state:N', alt.Tooltip('Amount (Cr):Q', format='.1f')]
     ).properties(title='Top 15 Cities by NCAP Funding – FY 2021-22', width=500, height=400)
     return chart


</code>
"
36,population_based,"Plot average PM2.5 (2021) vs state population as a scatter plot, labeling each point with the state name.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    pm = data[data['Timestamp'].dt.year == 2021].groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = pm.merge(states_data, on='state')
    points = alt.Chart(df).mark_point(filled=True, size=80).encode(
        x=alt.X('population:Q', title='Population', axis=alt.Axis(format='~s')),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('population:Q', format=',')]
    )
    labels = alt.Chart(df).mark_text(align='left', dx=5, fontSize=9).encode(
        x='population:Q', y='PM2\.5:Q', text='state:N'
    )
    return (points + labels).properties(title='PM2.5 vs Population by State – 2021', width=500, height=400)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     pm = data[data['Timestamp'].dt.year == 2021].groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = pm.merge(states_data, on='state')
     points = alt.Chart(df).mark_point(filled=True, size=80).encode(
         x=alt.X('population:Q', title='Population', axis=alt.Axis(format='~s')),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('population:Q', format=',')]
     )
     labels = alt.Chart(df).mark_text(align='left', dx=5, fontSize=9).encode(
         x='population:Q', y='PM2\.5:Q', text='state:N'
     )
     return (points + labels).properties(title='PM2.5 vs Population by State – 2021', width=500, height=400)


</code>
"
37,area_based,Show the PM2.5 per 1000 km² (air quality density) for each state in 2020 as a bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    pm = data[data['Timestamp'].dt.year == 2020].groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = pm.merge(states_data[['state','area (km2)']], on='state')
    df['PM2.5 per 1000 km²'] = df['PM2.5'] / df['area (km2)'] * 1000
    df = df.sort_values('PM2.5 per 1000 km²', ascending=False)
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5 per 1000 km²:Q', title='PM2.5 per 1000 km²'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM2\.5 per 1000 km²:Q', scale=alt.Scale(scheme='orangered'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5 per 1000 km²:Q', format='.3f')]
    ).properties(title='Air Quality Density (PM2.5 per 1000 km²) by State – 2020', width=500, height=400)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     pm = data[data['Timestamp'].dt.year == 2020].groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = pm.merge(states_data[['state','area (km2)']], on='state')
     df['PM2.5 per 1000 km²'] = df['PM2.5'] / df['area (km2)'] * 1000
     df = df.sort_values('PM2.5 per 1000 km²', ascending=False)
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5 per 1000 km²:Q', title='PM2.5 per 1000 km²'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM2\.5 per 1000 km²:Q', scale=alt.Scale(scheme='orangered'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5 per 1000 km²:Q', format='.3f')]
     ).properties(title='Air Quality Density (PM2.5 per 1000 km²) by State – 2020', width=500, height=400)
     return chart


</code>
"
38,area_based,"Create a bubble chart of PM2.5 vs area for each state in 2021, sized by population.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    pm = data[data['Timestamp'].dt.year == 2021].groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = pm.merge(states_data, on='state')
    chart = alt.Chart(df).mark_point(filled=True, opacity=0.7).encode(
        x=alt.X('area (km2):Q', title='Area (km²)', axis=alt.Axis(format='~s')),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        size=alt.Size('population:Q', title='Population', scale=alt.Scale(range=[50,1500])),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f'),
                 alt.Tooltip('population:Q', format=','), alt.Tooltip('area (km2):Q', format=',')]
    ).properties(title='PM2.5 vs Area (size=Population) – 2021', width=500, height=400)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     pm = data[data['Timestamp'].dt.year == 2021].groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = pm.merge(states_data, on='state')
     chart = alt.Chart(df).mark_point(filled=True, opacity=0.7).encode(
         x=alt.X('area (km2):Q', title='Area (km²)', axis=alt.Axis(format='~s')),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         size=alt.Size('population:Q', title='Population', scale=alt.Scale(range=[50,1500])),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f'),
                  alt.Tooltip('population:Q', format=','), alt.Tooltip('area (km2):Q', format=',')]
     ).properties(title='PM2.5 vs Area (size=Population) – 2021', width=500, height=400)
     return chart


</code>
"
39,population_based,Bar chart of PM2.5 per capita (average PM2.5 × 1000 / population) for each state in 2022.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    pm = data[data['Timestamp'].dt.year == 2022].groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = pm.merge(states_data[['state','population']], on='state')
    df['PM2.5 per Capita (×1000)'] = df['PM2.5'] / df['population'] * 1e6
    df = df.sort_values('PM2.5 per Capita (×1000)', ascending=False)
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5 per Capita (×1000):Q', title='PM2.5 per Million Population'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM2\.5 per Capita (×1000):Q', scale=alt.Scale(scheme='purples'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5 per Capita (×1000):Q', format='.4f')]
    ).properties(title='PM2.5 Per-Capita Pollution Index by State – 2022', width=500, height=400)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     pm = data[data['Timestamp'].dt.year == 2022].groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = pm.merge(states_data[['state','population']], on='state')
     df['PM2.5 per Capita (×1000)'] = df['PM2.5'] / df['population'] * 1e6
     df = df.sort_values('PM2.5 per Capita (×1000)', ascending=False)
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5 per Capita (×1000):Q', title='PM2.5 per Million Population'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM2\.5 per Capita (×1000):Q', scale=alt.Scale(scheme='purples'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5 per Capita (×1000):Q', format='.4f')]
     ).properties(title='PM2.5 Per-Capita Pollution Index by State – 2022', width=500, height=400)
     return chart


</code>
"
40,spatial_aggregation,Show a box plot of PM2.5 distribution for each state in 2020.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2020].dropna(subset=['PM2.5'])
    df = df[['state','PM2.5']]
    state_order = df.groupby('state')['PM2.5'].median().sort_values(ascending=False).index.tolist()
    chart = alt.Chart(df).mark_boxplot(extent='min-max').encode(
        x=alt.X('PM2\.5:Q', title='PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort=state_order, title='State'),
        color=alt.Color('state:N', legend=None)
    ).properties(title='PM2.5 Distribution by State – 2020', width=500, height=450)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2020].dropna(subset=['PM2.5'])
     df = df[['state','PM2.5']]
     state_order = df.groupby('state')['PM2.5'].median().sort_values(ascending=False).index.tolist()
     chart = alt.Chart(df).mark_boxplot(extent='min-max').encode(
         x=alt.X('PM2\.5:Q', title='PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort=state_order, title='State'),
         color=alt.Color('state:N', legend=None)
     ).properties(title='PM2.5 Distribution by State – 2020', width=500, height=450)
     return chart


</code>
"
41,spatial_aggregation,Plot the distribution of PM2.5 values in Delhi across all years using a histogram.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Delhi'].dropna(subset=['PM2.5'])
    df = df[['PM2.5']]
    chart = alt.Chart(df).mark_bar(color='steelblue', opacity=0.75).encode(
        x=alt.X('PM2\.5:Q', bin=alt.Bin(maxbins=40), title='PM2.5 (µg/m³)'),
        y=alt.Y('count()', title='Number of Observations'),
        tooltip=[alt.Tooltip('PM2\.5:Q', bin=True), 'count()']
    ).properties(title='PM2.5 Distribution – Delhi (All Years)', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Delhi'].dropna(subset=['PM2.5'])
     df = df[['PM2.5']]
     chart = alt.Chart(df).mark_bar(color='steelblue', opacity=0.75).encode(
         x=alt.X('PM2\.5:Q', bin=alt.Bin(maxbins=40), title='PM2.5 (µg/m³)'),
         y=alt.Y('count()', title='Number of Observations'),
         tooltip=[alt.Tooltip('PM2\.5:Q', bin=True), 'count()']
     ).properties(title='PM2.5 Distribution – Delhi (All Years)', width=500, height=300)
     return chart


</code>
"
42,temporal_aggregation,"Show a box plot of PM10 for each season (Winter, Summer, Monsoon, Post-Monsoon) for all of India.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    def season(m):
        if m in [12,1,2]: return 'Winter'
        elif m in [3,4,5]: return 'Summer'
        elif m in [6,7,8,9]: return 'Monsoon'
        else: return 'Post-Monsoon'
    df = data.dropna(subset=['PM10']).copy()
    df['Season'] = df['Timestamp'].dt.month.apply(season)
    df = df[['Season','PM10']]
    order = ['Winter','Post-Monsoon','Summer','Monsoon']
    chart = alt.Chart(df).mark_boxplot(extent='min-max').encode(
        x=alt.X('Season:N', sort=order, title='Season'),
        y=alt.Y('PM10:Q', title='PM10 (µg/m³)'),
        color=alt.Color('Season:N', legend=None)
    ).properties(title='PM10 Distribution by Season – India', width=450, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     def season(m):
         if m in [12,1,2]: return 'Winter'
         elif m in [3,4,5]: return 'Summer'
         elif m in [6,7,8,9]: return 'Monsoon'
         else: return 'Post-Monsoon'
     df = data.dropna(subset=['PM10']).copy()
     df['Season'] = df['Timestamp'].dt.month.apply(season)
     df = df[['Season','PM10']]
     order = ['Winter','Post-Monsoon','Summer','Monsoon']
     chart = alt.Chart(df).mark_boxplot(extent='min-max').encode(
         x=alt.X('Season:N', sort=order, title='Season'),
         y=alt.Y('PM10:Q', title='PM10 (µg/m³)'),
         color=alt.Color('Season:N', legend=None)
     ).properties(title='PM10 Distribution by Season – India', width=450, height=320)
     return chart


</code>
"
43,specific_pattern,Show PM2.5 exceedances above the Indian standard (60 µg/m³) per year as a bar chart for Delhi.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Delhi'].dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 60].copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby('Year')['Timestamp'].nunique().reset_index()
    df.columns = ['Year','Days Exceeded']
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('Days Exceeded:Q', title='Days PM2.5 > 60 µg/m³'),
        tooltip=['Year:N','Days Exceeded:Q']
    ).properties(title='Days Delhi Exceeded Indian PM2.5 Standard (60 µg/m³) per Year', width=450, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Delhi'].dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 60].copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby('Year')['Timestamp'].nunique().reset_index()
     df.columns = ['Year','Days Exceeded']
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('Days Exceeded:Q', title='Days PM2.5 > 60 µg/m³'),
         tooltip=['Year:N','Days Exceeded:Q']
     ).properties(title='Days Delhi Exceeded Indian PM2.5 Standard (60 µg/m³) per Year', width=450, height=300)
     return chart


</code>
"
44,specific_pattern,Plot the rolling 30-day average PM2.5 for Delhi in 2021 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Delhi') & (data['Timestamp'].dt.year == 2021)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Delhi 2021', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Delhi') & (data['Timestamp'].dt.year == 2021)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Delhi 2021', width=600, height=300)


</code>
"
45,specific_pattern,Show a cumulative area chart of PM2.5 readings for Hyderabad across 2020.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Hyderabad') & (data['Timestamp'].dt.year == 2020)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Hyderabad 2020', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Hyderabad') & (data['Timestamp'].dt.year == 2020)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Hyderabad 2020', width=600, height=300)
     return chart


</code>
"
46,spatial_aggregation,Plot the distribution of PM2.5 values in Bihar across all years using a histogram.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Bihar'].dropna(subset=['PM2.5'])
    df = df[['PM2.5']]
    chart = alt.Chart(df).mark_bar(color='steelblue', opacity=0.75).encode(
        x=alt.X('PM2\.5:Q', bin=alt.Bin(maxbins=40), title='PM2.5 (µg/m³)'),
        y=alt.Y('count()', title='Number of Observations'),
        tooltip=[alt.Tooltip('PM2\.5:Q', bin=True), 'count()']
    ).properties(title='PM2.5 Distribution – Bihar (All Years)', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Bihar'].dropna(subset=['PM2.5'])
     df = df[['PM2.5']]
     chart = alt.Chart(df).mark_bar(color='steelblue', opacity=0.75).encode(
         x=alt.X('PM2\.5:Q', bin=alt.Bin(maxbins=40), title='PM2.5 (µg/m³)'),
         y=alt.Y('count()', title='Number of Observations'),
         tooltip=[alt.Tooltip('PM2\.5:Q', bin=True), 'count()']
     ).properties(title='PM2.5 Distribution – Bihar (All Years)', width=500, height=300)
     return chart


</code>
"
47,spatial_aggregation,"Show the top 9 states by average PM10 in 2023 as a bar chart, sorted in descending order.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2023]
    df = df.groupby('state')['PM10'].mean().reset_index().dropna()
    df = df.nlargest(9, 'PM10')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Top 9 States by Average PM10 in 2023', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2023]
     df = df.groupby('state')['PM10'].mean().reset_index().dropna()
     df = df.nlargest(9, 'PM10')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Top 9 States by Average PM10 in 2023', width=500, height=300)
     return chart


</code>
"
48,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Rajasthan, Chandigarh, and Bihar in 2018 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Rajasthan', 'Chandigarh', 'Bihar']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2018)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Rajasthan, Chandigarh, UP – 2018', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Rajasthan', 'Chandigarh', 'Bihar']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2018)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Rajasthan, Chandigarh, UP – 2018', width=550, height=320)
     return chart


</code>
"
49,specific_pattern,Plot the rolling 30-day average PM2.5 for Uttar Pradesh in 2023 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Uttar Pradesh') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Uttar Pradesh 2023', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Uttar Pradesh') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Uttar Pradesh 2023', width=600, height=300)


</code>
"
50,funding_based,Visualize NCAP city-level funding for FY 2021-22 as a horizontal bar chart for the top 14 cities.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = ncap_funding_data[['city','state','Amount released during FY 2021-22']].copy()
    df.columns = ['city','state','Amount (Cr)']
    df = df.nlargest(14, 'Amount (Cr)')
    df['City_State'] = df['city'] + ', ' + df['state']
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Amount (Cr):Q', title='Amount Released (Cr)'),
        y=alt.Y('City_State:N', sort='-x', title='City, State'),
        color=alt.Color('state:N', title='State'),
        tooltip=['city:N','state:N', alt.Tooltip('Amount (Cr):Q', format='.1f')]
    ).properties(title='Top 14 Cities by NCAP Funding – FY 2021-22', width=500, height=400)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = ncap_funding_data[['city','state','Amount released during FY 2021-22']].copy()
     df.columns = ['city','state','Amount (Cr)']
     df = df.nlargest(14, 'Amount (Cr)')
     df['City_State'] = df['city'] + ', ' + df['state']
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Amount (Cr):Q', title='Amount Released (Cr)'),
         y=alt.Y('City_State:N', sort='-x', title='City, State'),
         color=alt.Color('state:N', title='State'),
         tooltip=['city:N','state:N', alt.Tooltip('Amount (Cr):Q', format='.1f')]
     ).properties(title='Top 14 Cities by NCAP Funding – FY 2021-22', width=500, height=400)
     return chart


</code>
"
51,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Andhra Pradesh, Delhi, and Assam across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Andhra Pradesh', 'Delhi', 'Assam']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Andhra Pradesh', 'Delhi', 'Assam']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
52,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Manipur, Andhra Pradesh, and Nagaland from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Manipur', 'Andhra Pradesh', 'Nagaland']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Manipur vs Andhra Pradesh vs Nagaland', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Manipur', 'Andhra Pradesh', 'Nagaland']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Manipur vs Andhra Pradesh vs Nagaland', width=550, height=320)
     return chart


</code>
"
53,population_based,"Plot average PM2.5 (2018) vs state population as a scatter plot, labeling each point with the state name.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    pm = data[data['Timestamp'].dt.year == 2018].groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = pm.merge(states_data, on='state')
    points = alt.Chart(df).mark_point(filled=True, size=80).encode(
        x=alt.X('population:Q', title='Population', axis=alt.Axis(format='~s')),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('population:Q', format=',')]
    )
    labels = alt.Chart(df).mark_text(align='left', dx=5, fontSize=9).encode(
        x='population:Q', y='PM2\.5:Q', text='state:N'
    )
    return (points + labels).properties(title='PM2.5 vs Population by State – 2018', width=500, height=400)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     pm = data[data['Timestamp'].dt.year == 2018].groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = pm.merge(states_data, on='state')
     points = alt.Chart(df).mark_point(filled=True, size=80).encode(
         x=alt.X('population:Q', title='Population', axis=alt.Axis(format='~s')),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('population:Q', format=',')]
     )
     labels = alt.Chart(df).mark_text(align='left', dx=5, fontSize=9).encode(
         x='population:Q', y='PM2\.5:Q', text='state:N'
     )
     return (points + labels).properties(title='PM2.5 vs Population by State – 2018', width=500, height=400)


</code>
"
54,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Madhya Pradesh, Haryana, and Mizoram from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Madhya Pradesh', 'Haryana', 'Mizoram']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Madhya Pradesh vs Haryana vs Mizoram', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Madhya Pradesh', 'Haryana', 'Mizoram']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Madhya Pradesh vs Haryana vs Mizoram', width=550, height=320)
     return chart


</code>
"
55,spatial_aggregation,Plot the top 7 states by average PM2.5 in 2024 as a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2024]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nlargest(7, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 7 States by Average PM2.5 in 2024', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2024]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nlargest(7, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 7 States by Average PM2.5 in 2024', width=500, height=300)
     return chart


</code>
"
56,specific_pattern,Show a cumulative area chart of PM2.5 readings for Mandideep across 2021.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Mandideep') & (data['Timestamp'].dt.year == 2021)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Mandideep 2021', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Mandideep') & (data['Timestamp'].dt.year == 2021)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Mandideep 2021', width=600, height=300)
     return chart


</code>
"
57,spatio_temporal_aggregation,Plot the yearly average PM2.5 for the top 7 most polluted states from 2017 to 2024 as a multi-line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    top5 = data.groupby('state')['PM2.5'].mean().nlargest(7).index.tolist()
    df = data[data['state'].isin(top5)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends – Top 7 Most Polluted States', width=600, height=350)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     top5 = data.groupby('state')['PM2.5'].mean().nlargest(7).index.tolist()
     df = data[data['state'].isin(top5)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends – Top 7 Most Polluted States', width=600, height=350)
     return chart


</code>
"
58,temporal_aggregation,Plot the weekly average PM2.5 for Malegaon in 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Malegaon') & (data['Timestamp'].dt.year == 2024)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Malegaon 2024', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Malegaon') & (data['Timestamp'].dt.year == 2024)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Malegaon 2024', width=600, height=300)
     return chart


</code>
"
59,spatial_aggregation,Show a bar chart comparing the 75th percentile PM2.5 of all states in winter 2017 (November–February).,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.month.isin([11,12,1,2])]
    df = df[df['Timestamp'].dt.year.isin([2017,2018])]
    df = df.groupby('state')['PM2.5'].quantile(0.75).reset_index().dropna()
    df.columns = ['state','PM2.5_p75']
    df = df.sort_values('PM2.5_p75', ascending=False)
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5_p75:Q', title='75th Percentile PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM2\.5_p75:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5_p75:Q', format='.1f', title='P75 PM2.5')]
    ).properties(title='75th Percentile PM2.5 by State – Winter 2017', width=500, height=400)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.month.isin([11,12,1,2])]
     df = df[df['Timestamp'].dt.year.isin([2017,2018])]
     df = df.groupby('state')['PM2.5'].quantile(0.75).reset_index().dropna()
     df.columns = ['state','PM2.5_p75']
     df = df.sort_values('PM2.5_p75', ascending=False)
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5_p75:Q', title='75th Percentile PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM2\.5_p75:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5_p75:Q', format='.1f', title='P75 PM2.5')]
     ).properties(title='75th Percentile PM2.5 by State – Winter 2017', width=500, height=400)
     return chart


</code>
"
60,spatial_aggregation,"Create a scatter plot of PM2.5 vs PM10 for all stations in 2017, colored by state.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2017]
    df = df.groupby(['station','state'])[['PM2.5','PM10']].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_point(opacity=0.6, size=60).encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['station:N','state:N',
                 alt.Tooltip('PM2\.5:Q', format='.1f'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='PM2.5 vs PM10 by Station – 2017', width=500, height=400)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2017]
     df = df.groupby(['station','state'])[['PM2.5','PM10']].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_point(opacity=0.6, size=60).encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['station:N','state:N',
                  alt.Tooltip('PM2\.5:Q', format='.1f'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='PM2.5 vs PM10 by Station – 2017', width=500, height=400)
     return chart


</code>
"
61,population_based,Bar chart of PM2.5 per capita (average PM2.5 × 1000 / population) for each state in 2019.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    pm = data[data['Timestamp'].dt.year == 2019].groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = pm.merge(states_data[['state','population']], on='state')
    df['PM2.5 per Capita (×1000)'] = df['PM2.5'] / df['population'] * 1e6
    df = df.sort_values('PM2.5 per Capita (×1000)', ascending=False)
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5 per Capita (×1000):Q', title='PM2.5 per Million Population'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM2\.5 per Capita (×1000):Q', scale=alt.Scale(scheme='purples'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5 per Capita (×1000):Q', format='.4f')]
    ).properties(title='PM2.5 Per-Capita Pollution Index by State – 2019', width=500, height=400)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     pm = data[data['Timestamp'].dt.year == 2019].groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = pm.merge(states_data[['state','population']], on='state')
     df['PM2.5 per Capita (×1000)'] = df['PM2.5'] / df['population'] * 1e6
     df = df.sort_values('PM2.5 per Capita (×1000)', ascending=False)
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5 per Capita (×1000):Q', title='PM2.5 per Million Population'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM2\.5 per Capita (×1000):Q', scale=alt.Scale(scheme='purples'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5 per Capita (×1000):Q', format='.4f')]
     ).properties(title='PM2.5 Per-Capita Pollution Index by State – 2019', width=500, height=400)
     return chart


</code>
"
62,spatial_aggregation,Visualize the bottom 12 states with the lowest average PM2.5 in 2023 using a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2023]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nsmallest(12, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Bottom 12 States by Average PM2.5 in 2023', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2023]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nsmallest(12, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Bottom 12 States by Average PM2.5 in 2023', width=500, height=300)
     return chart


</code>
"
63,funding_based,Visualize NCAP city-level funding for FY 2021-22 as a horizontal bar chart for the top 11 cities.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = ncap_funding_data[['city','state','Amount released during FY 2021-22']].copy()
    df.columns = ['city','state','Amount (Cr)']
    df = df.nlargest(11, 'Amount (Cr)')
    df['City_State'] = df['city'] + ', ' + df['state']
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Amount (Cr):Q', title='Amount Released (Cr)'),
        y=alt.Y('City_State:N', sort='-x', title='City, State'),
        color=alt.Color('state:N', title='State'),
        tooltip=['city:N','state:N', alt.Tooltip('Amount (Cr):Q', format='.1f')]
    ).properties(title='Top 11 Cities by NCAP Funding – FY 2021-22', width=500, height=400)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = ncap_funding_data[['city','state','Amount released during FY 2021-22']].copy()
     df.columns = ['city','state','Amount (Cr)']
     df = df.nlargest(11, 'Amount (Cr)')
     df['City_State'] = df['city'] + ', ' + df['state']
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Amount (Cr):Q', title='Amount Released (Cr)'),
         y=alt.Y('City_State:N', sort='-x', title='City, State'),
         color=alt.Color('state:N', title='State'),
         tooltip=['city:N','state:N', alt.Tooltip('Amount (Cr):Q', format='.1f')]
     ).properties(title='Top 11 Cities by NCAP Funding – FY 2021-22', width=500, height=400)
     return chart


</code>
"
64,spatial_aggregation,Plot the median PM10 for each state in summer 2022 (April–June) as a sorted bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month.isin([4,5,6]))]
    df = df.groupby('state')['PM10'].median().reset_index().dropna()
    df = df.sort_values('PM10', ascending=False)
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM10:Q', title='Median PM10 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM10:Q', scale=alt.Scale(scheme='yelloworangered'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Median PM10 by State – Summer 2022 (Apr–Jun)', width=500, height=400)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month.isin([4,5,6]))]
     df = df.groupby('state')['PM10'].median().reset_index().dropna()
     df = df.sort_values('PM10', ascending=False)
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM10:Q', title='Median PM10 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM10:Q', scale=alt.Scale(scheme='yelloworangered'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Median PM10 by State – Summer 2022 (Apr–Jun)', width=500, height=400)
     return chart


</code>
"
65,spatio_temporal_aggregation,Show the monthly average PM2.5 for Telangana across each year from 2017 to 2024 as small multiples (faceted by year).,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Telangana'].copy()
    df['Year'] = df['Timestamp'].dt.year
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='steelblue').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5'),
        tooltip=['Year:O','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).facet(
        facet=alt.Facet('Year:O', title='Year'),
        columns=4
    ).properties(title='Monthly PM2.5 in Telangana by Year (2017–2024)')
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Telangana'].copy()
     df['Year'] = df['Timestamp'].dt.year
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='steelblue').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5'),
         tooltip=['Year:O','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).facet(
         facet=alt.Facet('Year:O', title='Year'),
         columns=4
     ).properties(title='Monthly PM2.5 in Telangana by Year (2017–2024)')
     return chart


</code>
"
66,spatial_aggregation,Plot the distribution of PM2.5 values in Nagaland across all years using a histogram.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Nagaland'].dropna(subset=['PM2.5'])
    df = df[['PM2.5']]
    chart = alt.Chart(df).mark_bar(color='steelblue', opacity=0.75).encode(
        x=alt.X('PM2\.5:Q', bin=alt.Bin(maxbins=40), title='PM2.5 (µg/m³)'),
        y=alt.Y('count()', title='Number of Observations'),
        tooltip=[alt.Tooltip('PM2\.5:Q', bin=True), 'count()']
    ).properties(title='PM2.5 Distribution – Nagaland (All Years)', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Nagaland'].dropna(subset=['PM2.5'])
     df = df[['PM2.5']]
     chart = alt.Chart(df).mark_bar(color='steelblue', opacity=0.75).encode(
         x=alt.X('PM2\.5:Q', bin=alt.Bin(maxbins=40), title='PM2.5 (µg/m³)'),
         y=alt.Y('count()', title='Number of Observations'),
         tooltip=[alt.Tooltip('PM2\.5:Q', bin=True), 'count()']
     ).properties(title='PM2.5 Distribution – Nagaland (All Years)', width=500, height=300)
     return chart


</code>
"
67,spatio_temporal_aggregation,Show a heatmap of average PM2.5 for the top 8 most polluted states by month for 2022.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    top8 = data.groupby('state')['PM2.5'].mean().nlargest(8).index.tolist()
    df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_rect().encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('state:N', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
        tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Heatmap – Top 8 Polluted States by Month (2022)', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     top8 = data.groupby('state')['PM2.5'].mean().nlargest(8).index.tolist()
     df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_rect().encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('state:N', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
         tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Heatmap – Top 8 Polluted States by Month (2022)', width=500, height=300)
     return chart


</code>
"
68,spatial_aggregation,Show a bar chart of the top 15 cities by median PM2.5 in 2017.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2017]
    df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
    df = df.nlargest(15, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
        y=alt.Y('city:N', sort='-x', title='City'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
        tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 15 Cities by Median PM2.5 in 2017', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2017]
     df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
     df = df.nlargest(15, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
         y=alt.Y('city:N', sort='-x', title='City'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
         tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 15 Cities by Median PM2.5 in 2017', width=500, height=300)
     return chart


</code>
"
69,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Sikkim, Assam, and Himachal Pradesh from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Sikkim', 'Assam', 'Himachal Pradesh']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Sikkim vs Assam vs Himachal Pradesh', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Sikkim', 'Assam', 'Himachal Pradesh']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Sikkim vs Assam vs Himachal Pradesh', width=550, height=320)
     return chart


</code>
"
70,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Tripura, Karnataka, and Bihar from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Tripura', 'Karnataka', 'Bihar']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Tripura vs Karnataka vs Bihar', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Tripura', 'Karnataka', 'Bihar']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Tripura vs Karnataka vs Bihar', width=550, height=320)
     return chart


</code>
"
71,spatio_temporal_aggregation,Plot the yearly average PM2.5 for the top 12 most polluted states from 2017 to 2024 as a multi-line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    top5 = data.groupby('state')['PM2.5'].mean().nlargest(12).index.tolist()
    df = data[data['state'].isin(top5)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends – Top 12 Most Polluted States', width=600, height=350)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     top5 = data.groupby('state')['PM2.5'].mean().nlargest(12).index.tolist()
     df = data[data['state'].isin(top5)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends – Top 12 Most Polluted States', width=600, height=350)
     return chart


</code>
"
72,spatial_aggregation,Plot the distribution of PM2.5 values in Telangana across all years using a histogram.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Telangana'].dropna(subset=['PM2.5'])
    df = df[['PM2.5']]
    chart = alt.Chart(df).mark_bar(color='steelblue', opacity=0.75).encode(
        x=alt.X('PM2\.5:Q', bin=alt.Bin(maxbins=40), title='PM2.5 (µg/m³)'),
        y=alt.Y('count()', title='Number of Observations'),
        tooltip=[alt.Tooltip('PM2\.5:Q', bin=True), 'count()']
    ).properties(title='PM2.5 Distribution – Telangana (All Years)', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Telangana'].dropna(subset=['PM2.5'])
     df = df[['PM2.5']]
     chart = alt.Chart(df).mark_bar(color='steelblue', opacity=0.75).encode(
         x=alt.X('PM2\.5:Q', bin=alt.Bin(maxbins=40), title='PM2.5 (µg/m³)'),
         y=alt.Y('count()', title='Number of Observations'),
         tooltip=[alt.Tooltip('PM2\.5:Q', bin=True), 'count()']
     ).properties(title='PM2.5 Distribution – Telangana (All Years)', width=500, height=300)
     return chart


</code>
"
73,spatio_temporal_aggregation,Show a heatmap of average PM2.5 for the top 10 most polluted states by month for 2018.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    top8 = data.groupby('state')['PM2.5'].mean().nlargest(10).index.tolist()
    df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2018)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_rect().encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('state:N', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
        tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Heatmap – Top 10 Polluted States by Month (2018)', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     top8 = data.groupby('state')['PM2.5'].mean().nlargest(10).index.tolist()
     df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2018)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_rect().encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('state:N', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
         tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Heatmap – Top 10 Polluted States by Month (2018)', width=500, height=300)
     return chart


</code>
"
74,temporal_aggregation,Plot the weekly average PM2.5 for Kalaburagi in 2023 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Kalaburagi') & (data['Timestamp'].dt.year == 2023)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Kalaburagi 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Kalaburagi') & (data['Timestamp'].dt.year == 2023)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Kalaburagi 2023', width=600, height=300)
     return chart


</code>
"
75,specific_pattern,Show a cumulative area chart of PM2.5 readings for Belapur across 2023.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Belapur') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Belapur 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Belapur') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Belapur 2023', width=600, height=300)
     return chart


</code>
"
76,population_based,Bar chart of PM2.5 per capita (average PM2.5 × 1000 / population) for each state in 2023.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    pm = data[data['Timestamp'].dt.year == 2023].groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = pm.merge(states_data[['state','population']], on='state')
    df['PM2.5 per Capita (×1000)'] = df['PM2.5'] / df['population'] * 1e6
    df = df.sort_values('PM2.5 per Capita (×1000)', ascending=False)
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5 per Capita (×1000):Q', title='PM2.5 per Million Population'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM2\.5 per Capita (×1000):Q', scale=alt.Scale(scheme='purples'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5 per Capita (×1000):Q', format='.4f')]
    ).properties(title='PM2.5 Per-Capita Pollution Index by State – 2023', width=500, height=400)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     pm = data[data['Timestamp'].dt.year == 2023].groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = pm.merge(states_data[['state','population']], on='state')
     df['PM2.5 per Capita (×1000)'] = df['PM2.5'] / df['population'] * 1e6
     df = df.sort_values('PM2.5 per Capita (×1000)', ascending=False)
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5 per Capita (×1000):Q', title='PM2.5 per Million Population'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM2\.5 per Capita (×1000):Q', scale=alt.Scale(scheme='purples'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5 per Capita (×1000):Q', format='.4f')]
     ).properties(title='PM2.5 Per-Capita Pollution Index by State – 2023', width=500, height=400)
     return chart


</code>
"
77,temporal_aggregation,Show a monthly bar chart of the number of days Rajasthan exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2022.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Rajasthan') & (data['Timestamp'].dt.year == 2022)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Rajasthan Exceeded WHO PM2.5 Guideline per Month – 2022', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Rajasthan') & (data['Timestamp'].dt.year == 2022)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Rajasthan Exceeded WHO PM2.5 Guideline per Month – 2022', width=500, height=300)
     return chart


</code>
"
78,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Karnataka, Madhya Pradesh, and Haryana in 2018 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Karnataka', 'Madhya Pradesh', 'Haryana']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2018)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Karnataka, Madhya Pradesh, UP – 2018', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Karnataka', 'Madhya Pradesh', 'Haryana']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2018)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Karnataka, Madhya Pradesh, UP – 2018', width=550, height=320)
     return chart


</code>
"
79,spatial_aggregation,Plot the distribution of PM2.5 values in Punjab across all years using a histogram.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Punjab'].dropna(subset=['PM2.5'])
    df = df[['PM2.5']]
    chart = alt.Chart(df).mark_bar(color='steelblue', opacity=0.75).encode(
        x=alt.X('PM2\.5:Q', bin=alt.Bin(maxbins=40), title='PM2.5 (µg/m³)'),
        y=alt.Y('count()', title='Number of Observations'),
        tooltip=[alt.Tooltip('PM2\.5:Q', bin=True), 'count()']
    ).properties(title='PM2.5 Distribution – Punjab (All Years)', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Punjab'].dropna(subset=['PM2.5'])
     df = df[['PM2.5']]
     chart = alt.Chart(df).mark_bar(color='steelblue', opacity=0.75).encode(
         x=alt.X('PM2\.5:Q', bin=alt.Bin(maxbins=40), title='PM2.5 (µg/m³)'),
         y=alt.Y('count()', title='Number of Observations'),
         tooltip=[alt.Tooltip('PM2\.5:Q', bin=True), 'count()']
     ).properties(title='PM2.5 Distribution – Punjab (All Years)', width=500, height=300)
     return chart


</code>
"
80,specific_pattern,Plot the rolling 30-day average PM2.5 for Telangana in 2019 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Telangana') & (data['Timestamp'].dt.year == 2019)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Telangana 2019', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Telangana') & (data['Timestamp'].dt.year == 2019)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Telangana 2019', width=600, height=300)


</code>
"
81,spatial_aggregation,Plot the average PM2.5 across all states in February 2019 as a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['Timestamp'].dt.year == 2019) & (data['Timestamp'].dt.month == 2)]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('PM2.5', ascending=False)
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='plasma'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Average PM2.5 by State – February 2019', width=500, height=400)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['Timestamp'].dt.year == 2019) & (data['Timestamp'].dt.month == 2)]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('PM2.5', ascending=False)
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='plasma'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Average PM2.5 by State – February 2019', width=500, height=400)
     return chart


</code>
"
82,spatial_aggregation,Visualize the bottom 10 states with the lowest average PM2.5 in 2024 using a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2024]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nsmallest(10, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Bottom 10 States by Average PM2.5 in 2024', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2024]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nsmallest(10, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Bottom 10 States by Average PM2.5 in 2024', width=500, height=300)
     return chart


</code>
"
83,population_based,"Plot a scatter chart of state-level average PM2.5 versus population for 2019, with point size representing area.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    pm = data[data['Timestamp'].dt.year == 2019].groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = pm.merge(states_data, on='state')
    chart = alt.Chart(df).mark_point(filled=True, opacity=0.7).encode(
        x=alt.X('population:Q', title='Population', axis=alt.Axis(format='~s')),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        size=alt.Size('area (km2):Q', title='Area (km²)', scale=alt.Scale(range=[50,1000])),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f'),
                 alt.Tooltip('population:Q', format=','), 'area (km2):Q']
    ).properties(title='PM2.5 vs Population (size=Area) – 2019', width=500, height=400)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     pm = data[data['Timestamp'].dt.year == 2019].groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = pm.merge(states_data, on='state')
     chart = alt.Chart(df).mark_point(filled=True, opacity=0.7).encode(
         x=alt.X('population:Q', title='Population', axis=alt.Axis(format='~s')),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         size=alt.Size('area (km2):Q', title='Area (km²)', scale=alt.Scale(range=[50,1000])),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f'),
                  alt.Tooltip('population:Q', format=','), 'area (km2):Q']
     ).properties(title='PM2.5 vs Population (size=Area) – 2019', width=500, height=400)
     return chart


</code>
"
84,spatial_aggregation,Show a bar chart of the top 14 cities by median PM2.5 in 2018.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2018]
    df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
    df = df.nlargest(14, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
        y=alt.Y('city:N', sort='-x', title='City'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
        tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 14 Cities by Median PM2.5 in 2018', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2018]
     df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
     df = df.nlargest(14, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
         y=alt.Y('city:N', sort='-x', title='City'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
         tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 14 Cities by Median PM2.5 in 2018', width=500, height=300)
     return chart


</code>
"
85,spatio_temporal_aggregation,Create a grouped bar chart comparing the average PM2.5 in Winter vs Summer for the top 7 most polluted states.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    top6 = data.groupby('state')['PM2.5'].mean().nlargest(7).index.tolist()
    df = data[data['state'].isin(top6)].copy()
    df['Season'] = df['Timestamp'].dt.month.apply(
        lambda m: 'Winter' if m in [12,1,2] else ('Summer' if m in [3,4,5] else None))
    df = df.dropna(subset=['Season'])
    df = df.groupby(['state','Season'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('state:N', title='State'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('Season:N', title='Season'),
        xOffset='Season:N',
        tooltip=['state:N','Season:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Winter vs Summer PM2.5 – Top 7 Polluted States', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     top6 = data.groupby('state')['PM2.5'].mean().nlargest(7).index.tolist()
     df = data[data['state'].isin(top6)].copy()
     df['Season'] = df['Timestamp'].dt.month.apply(
         lambda m: 'Winter' if m in [12,1,2] else ('Summer' if m in [3,4,5] else None))
     df = df.dropna(subset=['Season'])
     df = df.groupby(['state','Season'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('state:N', title='State'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('Season:N', title='Season'),
         xOffset='Season:N',
         tooltip=['state:N','Season:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Winter vs Summer PM2.5 – Top 7 Polluted States', width=550, height=320)
     return chart


</code>
"
86,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Munger, Palkalaiperur, and Pudukottai in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Munger', 'Palkalaiperur', 'Pudukottai']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Munger vs Palkalaiperur vs Pudukottai – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Munger', 'Palkalaiperur', 'Pudukottai']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Munger vs Palkalaiperur vs Pudukottai – 2023', width=550, height=320)
     return chart


</code>
"
87,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Chandigarh, Maharashtra, and Manipur from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Chandigarh', 'Maharashtra', 'Manipur']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Chandigarh vs Maharashtra vs Manipur', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Chandigarh', 'Maharashtra', 'Manipur']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Chandigarh vs Maharashtra vs Manipur', width=550, height=320)
     return chart


</code>
"
88,spatial_aggregation,Plot the median PM10 for each state in summer 2024 (April–June) as a sorted bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['Timestamp'].dt.year == 2024) & (data['Timestamp'].dt.month.isin([4,5,6]))]
    df = df.groupby('state')['PM10'].median().reset_index().dropna()
    df = df.sort_values('PM10', ascending=False)
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM10:Q', title='Median PM10 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM10:Q', scale=alt.Scale(scheme='yelloworangered'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Median PM10 by State – Summer 2024 (Apr–Jun)', width=500, height=400)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['Timestamp'].dt.year == 2024) & (data['Timestamp'].dt.month.isin([4,5,6]))]
     df = df.groupby('state')['PM10'].median().reset_index().dropna()
     df = df.sort_values('PM10', ascending=False)
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM10:Q', title='Median PM10 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM10:Q', scale=alt.Scale(scheme='yelloworangered'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Median PM10 by State – Summer 2024 (Apr–Jun)', width=500, height=400)
     return chart


</code>
"
89,spatial_aggregation,Plot the average PM2.5 across all states in February 2024 as a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['Timestamp'].dt.year == 2024) & (data['Timestamp'].dt.month == 2)]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('PM2.5', ascending=False)
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='plasma'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Average PM2.5 by State – February 2024', width=500, height=400)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['Timestamp'].dt.year == 2024) & (data['Timestamp'].dt.month == 2)]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('PM2.5', ascending=False)
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='plasma'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Average PM2.5 by State – February 2024', width=500, height=400)
     return chart


</code>
"
90,temporal_aggregation,Plot the monthly average PM2.5 trend for Chandigarh from 2017 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Chandigarh'].copy()
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='steelblue').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly Average PM2.5 Trend – Chandigarh (2017–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Chandigarh'].copy()
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='steelblue').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly Average PM2.5 Trend – Chandigarh (2017–2024)', width=600, height=300)
     return chart


</code>
"
91,spatial_aggregation,Plot the distribution of PM2.5 values in Chhattisgarh across all years using a histogram.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Chhattisgarh'].dropna(subset=['PM2.5'])
    df = df[['PM2.5']]
    chart = alt.Chart(df).mark_bar(color='steelblue', opacity=0.75).encode(
        x=alt.X('PM2\.5:Q', bin=alt.Bin(maxbins=40), title='PM2.5 (µg/m³)'),
        y=alt.Y('count()', title='Number of Observations'),
        tooltip=[alt.Tooltip('PM2\.5:Q', bin=True), 'count()']
    ).properties(title='PM2.5 Distribution – Chhattisgarh (All Years)', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Chhattisgarh'].dropna(subset=['PM2.5'])
     df = df[['PM2.5']]
     chart = alt.Chart(df).mark_bar(color='steelblue', opacity=0.75).encode(
         x=alt.X('PM2\.5:Q', bin=alt.Bin(maxbins=40), title='PM2.5 (µg/m³)'),
         y=alt.Y('count()', title='Number of Observations'),
         tooltip=[alt.Tooltip('PM2\.5:Q', bin=True), 'count()']
     ).properties(title='PM2.5 Distribution – Chhattisgarh (All Years)', width=500, height=300)
     return chart


</code>
"
92,specific_pattern,Plot the rolling 30-day average PM2.5 for Nagaland in 2020 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Nagaland') & (data['Timestamp'].dt.year == 2020)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Nagaland 2020', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Nagaland') & (data['Timestamp'].dt.year == 2020)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Nagaland 2020', width=600, height=300)


</code>
"
93,spatial_aggregation,Show a bar chart of the top 11 cities by median PM2.5 in 2020.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2020]
    df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
    df = df.nlargest(11, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
        y=alt.Y('city:N', sort='-x', title='City'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
        tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 11 Cities by Median PM2.5 in 2020', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2020]
     df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
     df = df.nlargest(11, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
         y=alt.Y('city:N', sort='-x', title='City'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
         tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 11 Cities by Median PM2.5 in 2020', width=500, height=300)
     return chart


</code>
"
94,population_based,Bar chart of PM2.5 per capita (average PM2.5 × 1000 / population) for each state in 2020.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    pm = data[data['Timestamp'].dt.year == 2020].groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = pm.merge(states_data[['state','population']], on='state')
    df['PM2.5 per Capita (×1000)'] = df['PM2.5'] / df['population'] * 1e6
    df = df.sort_values('PM2.5 per Capita (×1000)', ascending=False)
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5 per Capita (×1000):Q', title='PM2.5 per Million Population'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM2\.5 per Capita (×1000):Q', scale=alt.Scale(scheme='purples'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5 per Capita (×1000):Q', format='.4f')]
    ).properties(title='PM2.5 Per-Capita Pollution Index by State – 2020', width=500, height=400)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     pm = data[data['Timestamp'].dt.year == 2020].groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = pm.merge(states_data[['state','population']], on='state')
     df['PM2.5 per Capita (×1000)'] = df['PM2.5'] / df['population'] * 1e6
     df = df.sort_values('PM2.5 per Capita (×1000)', ascending=False)
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5 per Capita (×1000):Q', title='PM2.5 per Million Population'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM2\.5 per Capita (×1000):Q', scale=alt.Scale(scheme='purples'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5 per Capita (×1000):Q', format='.4f')]
     ).properties(title='PM2.5 Per-Capita Pollution Index by State – 2020', width=500, height=400)
     return chart


</code>
"
95,funding_based,Show a scatter plot of total NCAP funding vs average PM2.5 (2019) per state.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    pm = data[data['Timestamp'].dt.year == 2019].groupby('state')['PM2.5'].mean().reset_index()
    funding = ncap_funding_data.groupby('state')['Total fund released'].sum().reset_index()
    df = pm.merge(funding, on='state').dropna()
    chart = alt.Chart(df).mark_point(filled=True, size=100).encode(
        x=alt.X('Total fund released:Q', title='Total NCAP Funding (Cr)'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 – 2019 (µg/m³)'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f'),
                 alt.Tooltip('Total fund released:Q', format='.1f')]
    ).properties(title='NCAP Funding vs Average PM2.5 by State (2019)', width=450, height=350)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     pm = data[data['Timestamp'].dt.year == 2019].groupby('state')['PM2.5'].mean().reset_index()
     funding = ncap_funding_data.groupby('state')['Total fund released'].sum().reset_index()
     df = pm.merge(funding, on='state').dropna()
     chart = alt.Chart(df).mark_point(filled=True, size=100).encode(
         x=alt.X('Total fund released:Q', title='Total NCAP Funding (Cr)'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 – 2019 (µg/m³)'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f'),
                  alt.Tooltip('Total fund released:Q', format='.1f')]
     ).properties(title='NCAP Funding vs Average PM2.5 by State (2019)', width=450, height=350)
     return chart


</code>
"
96,population_based,"Plot average PM2.5 (2024) vs state population as a scatter plot, labeling each point with the state name.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    pm = data[data['Timestamp'].dt.year == 2024].groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = pm.merge(states_data, on='state')
    points = alt.Chart(df).mark_point(filled=True, size=80).encode(
        x=alt.X('population:Q', title='Population', axis=alt.Axis(format='~s')),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('population:Q', format=',')]
    )
    labels = alt.Chart(df).mark_text(align='left', dx=5, fontSize=9).encode(
        x='population:Q', y='PM2\.5:Q', text='state:N'
    )
    return (points + labels).properties(title='PM2.5 vs Population by State – 2024', width=500, height=400)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     pm = data[data['Timestamp'].dt.year == 2024].groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = pm.merge(states_data, on='state')
     points = alt.Chart(df).mark_point(filled=True, size=80).encode(
         x=alt.X('population:Q', title='Population', axis=alt.Axis(format='~s')),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('population:Q', format=',')]
     )
     labels = alt.Chart(df).mark_text(align='left', dx=5, fontSize=9).encode(
         x='population:Q', y='PM2\.5:Q', text='state:N'
     )
     return (points + labels).properties(title='PM2.5 vs Population by State – 2024', width=500, height=400)


</code>
"
97,spatial_aggregation,Plot the top 6 states by average PM2.5 in 2023 as a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2023]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nlargest(6, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 6 States by Average PM2.5 in 2023', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2023]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nlargest(6, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 6 States by Average PM2.5 in 2023', width=500, height=300)
     return chart


</code>
"
98,specific_pattern,Plot the rolling 30-day average PM2.5 for Sikkim in 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Sikkim') & (data['Timestamp'].dt.year == 2022)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Sikkim 2022', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Sikkim') & (data['Timestamp'].dt.year == 2022)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Sikkim 2022', width=600, height=300)


</code>
"
99,spatio_temporal_aggregation,Plot the yearly average PM2.5 for the top 15 most polluted states from 2017 to 2024 as a multi-line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    top5 = data.groupby('state')['PM2.5'].mean().nlargest(15).index.tolist()
    df = data[data['state'].isin(top5)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends – Top 15 Most Polluted States', width=600, height=350)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     top5 = data.groupby('state')['PM2.5'].mean().nlargest(15).index.tolist()
     df = data[data['state'].isin(top5)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends – Top 15 Most Polluted States', width=600, height=350)
     return chart


</code>
"
100,spatio_temporal_aggregation,Plot a heatmap of average PM10 by state (y-axis) and month (x-axis) for 2017.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2017].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['state','Month'])['PM10'].mean().reset_index().dropna()
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_rect().encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('state:N', title='State'),
        color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), title='Avg PM10'),
        tooltip=['state:N','MonthName:N', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='PM10 Heatmap by State and Month – 2017', width=500, height=400)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2017].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['state','Month'])['PM10'].mean().reset_index().dropna()
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_rect().encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('state:N', title='State'),
         color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), title='Avg PM10'),
         tooltip=['state:N','MonthName:N', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='PM10 Heatmap by State and Month – 2017', width=500, height=400)
     return chart


</code>
"
101,spatio_temporal_aggregation,Show the monthly average PM2.5 for Kerala across each year from 2017 to 2024 as small multiples (faceted by year).,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Kerala'].copy()
    df['Year'] = df['Timestamp'].dt.year
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='steelblue').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5'),
        tooltip=['Year:O','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).facet(
        facet=alt.Facet('Year:O', title='Year'),
        columns=4
    ).properties(title='Monthly PM2.5 in Kerala by Year (2017–2024)')
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Kerala'].copy()
     df['Year'] = df['Timestamp'].dt.year
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='steelblue').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5'),
         tooltip=['Year:O','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).facet(
         facet=alt.Facet('Year:O', title='Year'),
         columns=4
     ).properties(title='Monthly PM2.5 in Kerala by Year (2017–2024)')
     return chart


</code>
"
102,temporal_aggregation,Show a monthly bar chart of the number of days Andhra Pradesh exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2021.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Andhra Pradesh') & (data['Timestamp'].dt.year == 2021)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Andhra Pradesh Exceeded WHO PM2.5 Guideline per Month – 2021', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Andhra Pradesh') & (data['Timestamp'].dt.year == 2021)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Andhra Pradesh Exceeded WHO PM2.5 Guideline per Month – 2021', width=500, height=300)
     return chart


</code>
"
103,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Boisar, Surat, and Durgapur in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Boisar', 'Surat', 'Durgapur']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Boisar vs Surat vs Durgapur – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Boisar', 'Surat', 'Durgapur']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Boisar vs Surat vs Durgapur – 2022', width=550, height=320)
     return chart


</code>
"
104,spatial_aggregation,Plot the distribution of PM2.5 values in Himachal Pradesh across all years using a histogram.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Himachal Pradesh'].dropna(subset=['PM2.5'])
    df = df[['PM2.5']]
    chart = alt.Chart(df).mark_bar(color='steelblue', opacity=0.75).encode(
        x=alt.X('PM2\.5:Q', bin=alt.Bin(maxbins=40), title='PM2.5 (µg/m³)'),
        y=alt.Y('count()', title='Number of Observations'),
        tooltip=[alt.Tooltip('PM2\.5:Q', bin=True), 'count()']
    ).properties(title='PM2.5 Distribution – Himachal Pradesh (All Years)', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Himachal Pradesh'].dropna(subset=['PM2.5'])
     df = df[['PM2.5']]
     chart = alt.Chart(df).mark_bar(color='steelblue', opacity=0.75).encode(
         x=alt.X('PM2\.5:Q', bin=alt.Bin(maxbins=40), title='PM2.5 (µg/m³)'),
         y=alt.Y('count()', title='Number of Observations'),
         tooltip=[alt.Tooltip('PM2\.5:Q', bin=True), 'count()']
     ).properties(title='PM2.5 Distribution – Himachal Pradesh (All Years)', width=500, height=300)
     return chart


</code>
"
105,spatial_aggregation,Plot the distribution of PM2.5 values in Manipur across all years using a histogram.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Manipur'].dropna(subset=['PM2.5'])
    df = df[['PM2.5']]
    chart = alt.Chart(df).mark_bar(color='steelblue', opacity=0.75).encode(
        x=alt.X('PM2\.5:Q', bin=alt.Bin(maxbins=40), title='PM2.5 (µg/m³)'),
        y=alt.Y('count()', title='Number of Observations'),
        tooltip=[alt.Tooltip('PM2\.5:Q', bin=True), 'count()']
    ).properties(title='PM2.5 Distribution – Manipur (All Years)', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Manipur'].dropna(subset=['PM2.5'])
     df = df[['PM2.5']]
     chart = alt.Chart(df).mark_bar(color='steelblue', opacity=0.75).encode(
         x=alt.X('PM2\.5:Q', bin=alt.Bin(maxbins=40), title='PM2.5 (µg/m³)'),
         y=alt.Y('count()', title='Number of Observations'),
         tooltip=[alt.Tooltip('PM2\.5:Q', bin=True), 'count()']
     ).properties(title='PM2.5 Distribution – Manipur (All Years)', width=500, height=300)
     return chart


</code>
"
106,population_based,Bar chart of PM2.5 per capita (average PM2.5 × 1000 / population) for each state in 2018.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    pm = data[data['Timestamp'].dt.year == 2018].groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = pm.merge(states_data[['state','population']], on='state')
    df['PM2.5 per Capita (×1000)'] = df['PM2.5'] / df['population'] * 1e6
    df = df.sort_values('PM2.5 per Capita (×1000)', ascending=False)
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5 per Capita (×1000):Q', title='PM2.5 per Million Population'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM2\.5 per Capita (×1000):Q', scale=alt.Scale(scheme='purples'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5 per Capita (×1000):Q', format='.4f')]
    ).properties(title='PM2.5 Per-Capita Pollution Index by State – 2018', width=500, height=400)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     pm = data[data['Timestamp'].dt.year == 2018].groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = pm.merge(states_data[['state','population']], on='state')
     df['PM2.5 per Capita (×1000)'] = df['PM2.5'] / df['population'] * 1e6
     df = df.sort_values('PM2.5 per Capita (×1000)', ascending=False)
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5 per Capita (×1000):Q', title='PM2.5 per Million Population'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM2\.5 per Capita (×1000):Q', scale=alt.Scale(scheme='purples'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5 per Capita (×1000):Q', format='.4f')]
     ).properties(title='PM2.5 Per-Capita Pollution Index by State – 2018', width=500, height=400)
     return chart


</code>
"
107,spatio_temporal_aggregation,Show a heatmap of average PM2.5 for the top 7 most polluted states by month for 2024.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    top8 = data.groupby('state')['PM2.5'].mean().nlargest(7).index.tolist()
    df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_rect().encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('state:N', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
        tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Heatmap – Top 7 Polluted States by Month (2024)', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     top8 = data.groupby('state')['PM2.5'].mean().nlargest(7).index.tolist()
     df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_rect().encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('state:N', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
         tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Heatmap – Top 7 Polluted States by Month (2024)', width=500, height=300)
     return chart


</code>
"
108,funding_based,Visualize NCAP city-level funding for FY 2021-22 as a horizontal bar chart for the top 13 cities.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = ncap_funding_data[['city','state','Amount released during FY 2021-22']].copy()
    df.columns = ['city','state','Amount (Cr)']
    df = df.nlargest(13, 'Amount (Cr)')
    df['City_State'] = df['city'] + ', ' + df['state']
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Amount (Cr):Q', title='Amount Released (Cr)'),
        y=alt.Y('City_State:N', sort='-x', title='City, State'),
        color=alt.Color('state:N', title='State'),
        tooltip=['city:N','state:N', alt.Tooltip('Amount (Cr):Q', format='.1f')]
    ).properties(title='Top 13 Cities by NCAP Funding – FY 2021-22', width=500, height=400)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = ncap_funding_data[['city','state','Amount released during FY 2021-22']].copy()
     df.columns = ['city','state','Amount (Cr)']
     df = df.nlargest(13, 'Amount (Cr)')
     df['City_State'] = df['city'] + ', ' + df['state']
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Amount (Cr):Q', title='Amount Released (Cr)'),
         y=alt.Y('City_State:N', sort='-x', title='City, State'),
         color=alt.Color('state:N', title='State'),
         tooltip=['city:N','state:N', alt.Tooltip('Amount (Cr):Q', format='.1f')]
     ).properties(title='Top 13 Cities by NCAP Funding – FY 2021-22', width=500, height=400)
     return chart


</code>
"
109,spatial_aggregation,Plot the top 10 states by average PM2.5 in 2022 as a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2022]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nlargest(10, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 10 States by Average PM2.5 in 2022', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2022]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nlargest(10, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 10 States by Average PM2.5 in 2022', width=500, height=300)
     return chart


</code>
"
110,temporal_aggregation,Plot the monthly average PM2.5 trend for Uttarakhand from 2017 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Uttarakhand'].copy()
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='steelblue').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly Average PM2.5 Trend – Uttarakhand (2017–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Uttarakhand'].copy()
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='steelblue').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly Average PM2.5 Trend – Uttarakhand (2017–2024)', width=600, height=300)
     return chart


</code>
"
111,spatio_temporal_aggregation,Show a heatmap of average PM2.5 for the top 9 most polluted states by month for 2024.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    top8 = data.groupby('state')['PM2.5'].mean().nlargest(9).index.tolist()
    df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_rect().encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('state:N', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
        tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Heatmap – Top 9 Polluted States by Month (2024)', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     top8 = data.groupby('state')['PM2.5'].mean().nlargest(9).index.tolist()
     df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_rect().encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('state:N', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
         tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Heatmap – Top 9 Polluted States by Month (2024)', width=500, height=300)
     return chart


</code>
"
112,spatial_aggregation,Plot the median PM10 for each state in summer 2017 (April–June) as a sorted bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['Timestamp'].dt.year == 2017) & (data['Timestamp'].dt.month.isin([4,5,6]))]
    df = df.groupby('state')['PM10'].median().reset_index().dropna()
    df = df.sort_values('PM10', ascending=False)
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM10:Q', title='Median PM10 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM10:Q', scale=alt.Scale(scheme='yelloworangered'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Median PM10 by State – Summer 2017 (Apr–Jun)', width=500, height=400)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['Timestamp'].dt.year == 2017) & (data['Timestamp'].dt.month.isin([4,5,6]))]
     df = df.groupby('state')['PM10'].median().reset_index().dropna()
     df = df.sort_values('PM10', ascending=False)
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM10:Q', title='Median PM10 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM10:Q', scale=alt.Scale(scheme='yelloworangered'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Median PM10 by State – Summer 2017 (Apr–Jun)', width=500, height=400)
     return chart


</code>
"
113,spatial_aggregation,Show a bar chart of the top 13 cities by median PM2.5 in 2024.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2024]
    df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
    df = df.nlargest(13, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
        y=alt.Y('city:N', sort='-x', title='City'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
        tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 13 Cities by Median PM2.5 in 2024', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2024]
     df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
     df = df.nlargest(13, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
         y=alt.Y('city:N', sort='-x', title='City'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
         tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 13 Cities by Median PM2.5 in 2024', width=500, height=300)
     return chart


</code>
"
114,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Ujjain, Hajipur, and Pratapgarh in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Ujjain', 'Hajipur', 'Pratapgarh']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Ujjain vs Hajipur vs Pratapgarh – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Ujjain', 'Hajipur', 'Pratapgarh']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Ujjain vs Hajipur vs Pratapgarh – 2022', width=550, height=320)
     return chart


</code>
"
115,population_based,"Plot a scatter chart of state-level average PM2.5 versus population for 2023, with point size representing area.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    pm = data[data['Timestamp'].dt.year == 2023].groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = pm.merge(states_data, on='state')
    chart = alt.Chart(df).mark_point(filled=True, opacity=0.7).encode(
        x=alt.X('population:Q', title='Population', axis=alt.Axis(format='~s')),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        size=alt.Size('area (km2):Q', title='Area (km²)', scale=alt.Scale(range=[50,1000])),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f'),
                 alt.Tooltip('population:Q', format=','), 'area (km2):Q']
    ).properties(title='PM2.5 vs Population (size=Area) – 2023', width=500, height=400)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     pm = data[data['Timestamp'].dt.year == 2023].groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = pm.merge(states_data, on='state')
     chart = alt.Chart(df).mark_point(filled=True, opacity=0.7).encode(
         x=alt.X('population:Q', title='Population', axis=alt.Axis(format='~s')),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         size=alt.Size('area (km2):Q', title='Area (km²)', scale=alt.Scale(range=[50,1000])),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f'),
                  alt.Tooltip('population:Q', format=','), 'area (km2):Q']
     ).properties(title='PM2.5 vs Population (size=Area) – 2023', width=500, height=400)
     return chart


</code>
"
116,spatio_temporal_aggregation,Show a heatmap of average PM2.5 for the top 13 most polluted states by month for 2021.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    top8 = data.groupby('state')['PM2.5'].mean().nlargest(13).index.tolist()
    df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2021)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_rect().encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('state:N', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
        tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Heatmap – Top 13 Polluted States by Month (2021)', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     top8 = data.groupby('state')['PM2.5'].mean().nlargest(13).index.tolist()
     df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2021)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_rect().encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('state:N', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
         tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Heatmap – Top 13 Polluted States by Month (2021)', width=500, height=300)
     return chart


</code>
"
117,spatio_temporal_aggregation,Plot a grouped bar chart of average PM10 by season and year (2017–2020) for all of India.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    def season(m):
        if m in [12,1,2]: return 'Winter'
        elif m in [3,4,5]: return 'Summer'
        elif m in [6,7,8,9]: return 'Monsoon'
        else: return 'Post-Monsoon'
    df = data[data['Timestamp'].dt.year.isin([2017,2018,2019,2020])].copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df['Season'] = df['Timestamp'].dt.month.apply(season)
    df = df.groupby(['Season','Year'])['PM10'].mean().reset_index().dropna()
    order = ['Winter','Summer','Post-Monsoon','Monsoon']
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Season:N', sort=order, title='Season'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('Year:N', title='Year'),
        xOffset='Year:N',
        tooltip=['Season:N','Year:N', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Average PM10 by Season and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     def season(m):
         if m in [12,1,2]: return 'Winter'
         elif m in [3,4,5]: return 'Summer'
         elif m in [6,7,8,9]: return 'Monsoon'
         else: return 'Post-Monsoon'
     df = data[data['Timestamp'].dt.year.isin([2017,2018,2019,2020])].copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df['Season'] = df['Timestamp'].dt.month.apply(season)
     df = df.groupby(['Season','Year'])['PM10'].mean().reset_index().dropna()
     order = ['Winter','Summer','Post-Monsoon','Monsoon']
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Season:N', sort=order, title='Season'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('Year:N', title='Year'),
         xOffset='Year:N',
         tooltip=['Season:N','Year:N', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Average PM10 by Season and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
118,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Jammu and Kashmir, Assam, and Andhra Pradesh in 2018 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Jammu and Kashmir', 'Assam', 'Andhra Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2018)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Jammu and Kashmir, Assam, UP – 2018', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Jammu and Kashmir', 'Assam', 'Andhra Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2018)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Jammu and Kashmir, Assam, UP – 2018', width=550, height=320)
     return chart


</code>
"
119,area_based,"Create a bubble chart of PM2.5 vs area for each state in 2022, sized by population.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    pm = data[data['Timestamp'].dt.year == 2022].groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = pm.merge(states_data, on='state')
    chart = alt.Chart(df).mark_point(filled=True, opacity=0.7).encode(
        x=alt.X('area (km2):Q', title='Area (km²)', axis=alt.Axis(format='~s')),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        size=alt.Size('population:Q', title='Population', scale=alt.Scale(range=[50,1500])),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f'),
                 alt.Tooltip('population:Q', format=','), alt.Tooltip('area (km2):Q', format=',')]
    ).properties(title='PM2.5 vs Area (size=Population) – 2022', width=500, height=400)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     pm = data[data['Timestamp'].dt.year == 2022].groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = pm.merge(states_data, on='state')
     chart = alt.Chart(df).mark_point(filled=True, opacity=0.7).encode(
         x=alt.X('area (km2):Q', title='Area (km²)', axis=alt.Axis(format='~s')),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         size=alt.Size('population:Q', title='Population', scale=alt.Scale(range=[50,1500])),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f'),
                  alt.Tooltip('population:Q', format=','), alt.Tooltip('area (km2):Q', format=',')]
     ).properties(title='PM2.5 vs Area (size=Population) – 2022', width=500, height=400)
     return chart


</code>
"
120,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Mizoram, Andhra Pradesh, and Gujarat from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Mizoram', 'Andhra Pradesh', 'Gujarat']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Mizoram vs Andhra Pradesh vs Gujarat', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Mizoram', 'Andhra Pradesh', 'Gujarat']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Mizoram vs Andhra Pradesh vs Gujarat', width=550, height=320)
     return chart


</code>
"
121,spatial_aggregation,Show a bar chart of the top 15 cities by median PM2.5 in 2023.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2023]
    df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
    df = df.nlargest(15, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
        y=alt.Y('city:N', sort='-x', title='City'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
        tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 15 Cities by Median PM2.5 in 2023', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2023]
     df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
     df = df.nlargest(15, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
         y=alt.Y('city:N', sort='-x', title='City'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
         tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 15 Cities by Median PM2.5 in 2023', width=500, height=300)
     return chart


</code>
"
122,spatial_aggregation,Show a bar chart of the top 10 cities by median PM2.5 in 2017.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2017]
    df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
    df = df.nlargest(10, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
        y=alt.Y('city:N', sort='-x', title='City'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
        tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 10 Cities by Median PM2.5 in 2017', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2017]
     df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
     df = df.nlargest(10, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
         y=alt.Y('city:N', sort='-x', title='City'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
         tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 10 Cities by Median PM2.5 in 2017', width=500, height=300)
     return chart


</code>
"
123,spatial_aggregation,Show a bar chart of the top 8 cities by median PM2.5 in 2021.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2021]
    df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
    df = df.nlargest(8, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
        y=alt.Y('city:N', sort='-x', title='City'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
        tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 8 Cities by Median PM2.5 in 2021', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2021]
     df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
     df = df.nlargest(8, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
         y=alt.Y('city:N', sort='-x', title='City'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
         tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 8 Cities by Median PM2.5 in 2021', width=500, height=300)
     return chart


</code>
"
124,area_based,Show the PM2.5 per 1000 km² (air quality density) for each state in 2021 as a bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    pm = data[data['Timestamp'].dt.year == 2021].groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = pm.merge(states_data[['state','area (km2)']], on='state')
    df['PM2.5 per 1000 km²'] = df['PM2.5'] / df['area (km2)'] * 1000
    df = df.sort_values('PM2.5 per 1000 km²', ascending=False)
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5 per 1000 km²:Q', title='PM2.5 per 1000 km²'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM2\.5 per 1000 km²:Q', scale=alt.Scale(scheme='orangered'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5 per 1000 km²:Q', format='.3f')]
    ).properties(title='Air Quality Density (PM2.5 per 1000 km²) by State – 2021', width=500, height=400)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     pm = data[data['Timestamp'].dt.year == 2021].groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = pm.merge(states_data[['state','area (km2)']], on='state')
     df['PM2.5 per 1000 km²'] = df['PM2.5'] / df['area (km2)'] * 1000
     df = df.sort_values('PM2.5 per 1000 km²', ascending=False)
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5 per 1000 km²:Q', title='PM2.5 per 1000 km²'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM2\.5 per 1000 km²:Q', scale=alt.Scale(scheme='orangered'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5 per 1000 km²:Q', format='.3f')]
     ).properties(title='Air Quality Density (PM2.5 per 1000 km²) by State – 2021', width=500, height=400)
     return chart


</code>
"
125,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Tamil Nadu, Maharashtra, and Kerala in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Tamil Nadu', 'Maharashtra', 'Kerala']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Tamil Nadu, Maharashtra, UP – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Tamil Nadu', 'Maharashtra', 'Kerala']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Tamil Nadu, Maharashtra, UP – 2024', width=550, height=320)
     return chart


</code>
"
126,temporal_aggregation,Show the monthly average PM2.5 for Bidar in 2017 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Bidar') & (data['Timestamp'].dt.year == 2017)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Bidar 2017', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Bidar') & (data['Timestamp'].dt.year == 2017)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Bidar 2017', width=450, height=280)


</code>
"
127,specific_pattern,Show PM2.5 exceedances above the Indian standard (60 µg/m³) per year as a bar chart for Kerala.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Kerala'].dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 60].copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby('Year')['Timestamp'].nunique().reset_index()
    df.columns = ['Year','Days Exceeded']
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('Days Exceeded:Q', title='Days PM2.5 > 60 µg/m³'),
        tooltip=['Year:N','Days Exceeded:Q']
    ).properties(title='Days Kerala Exceeded Indian PM2.5 Standard (60 µg/m³) per Year', width=450, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Kerala'].dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 60].copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby('Year')['Timestamp'].nunique().reset_index()
     df.columns = ['Year','Days Exceeded']
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('Days Exceeded:Q', title='Days PM2.5 > 60 µg/m³'),
         tooltip=['Year:N','Days Exceeded:Q']
     ).properties(title='Days Kerala Exceeded Indian PM2.5 Standard (60 µg/m³) per Year', width=450, height=300)
     return chart


</code>
"
128,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Madhya Pradesh, Rajasthan, and Tripura across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Madhya Pradesh', 'Rajasthan', 'Tripura']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Madhya Pradesh', 'Rajasthan', 'Tripura']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
129,spatial_aggregation,Plot the average PM2.5 across all states in February 2023 as a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['Timestamp'].dt.year == 2023) & (data['Timestamp'].dt.month == 2)]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('PM2.5', ascending=False)
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='plasma'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Average PM2.5 by State – February 2023', width=500, height=400)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['Timestamp'].dt.year == 2023) & (data['Timestamp'].dt.month == 2)]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('PM2.5', ascending=False)
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='plasma'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Average PM2.5 by State – February 2023', width=500, height=400)
     return chart


</code>
"
130,spatio_temporal_aggregation,Plot a heatmap of average PM10 by state (y-axis) and month (x-axis) for 2024.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2024].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['state','Month'])['PM10'].mean().reset_index().dropna()
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_rect().encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('state:N', title='State'),
        color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), title='Avg PM10'),
        tooltip=['state:N','MonthName:N', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='PM10 Heatmap by State and Month – 2024', width=500, height=400)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2024].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['state','Month'])['PM10'].mean().reset_index().dropna()
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_rect().encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('state:N', title='State'),
         color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), title='Avg PM10'),
         tooltip=['state:N','MonthName:N', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='PM10 Heatmap by State and Month – 2024', width=500, height=400)
     return chart


</code>
"
131,spatial_aggregation,Show a bar chart comparing the 75th percentile PM2.5 of all states in winter 2018 (November–February).,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.month.isin([11,12,1,2])]
    df = df[df['Timestamp'].dt.year.isin([2018,2019])]
    df = df.groupby('state')['PM2.5'].quantile(0.75).reset_index().dropna()
    df.columns = ['state','PM2.5_p75']
    df = df.sort_values('PM2.5_p75', ascending=False)
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5_p75:Q', title='75th Percentile PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM2\.5_p75:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5_p75:Q', format='.1f', title='P75 PM2.5')]
    ).properties(title='75th Percentile PM2.5 by State – Winter 2018', width=500, height=400)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.month.isin([11,12,1,2])]
     df = df[df['Timestamp'].dt.year.isin([2018,2019])]
     df = df.groupby('state')['PM2.5'].quantile(0.75).reset_index().dropna()
     df.columns = ['state','PM2.5_p75']
     df = df.sort_values('PM2.5_p75', ascending=False)
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5_p75:Q', title='75th Percentile PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM2\.5_p75:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5_p75:Q', format='.1f', title='P75 PM2.5')]
     ).properties(title='75th Percentile PM2.5 by State – Winter 2018', width=500, height=400)
     return chart


</code>
"
132,temporal_aggregation,Show the monthly average PM2.5 for Udaipur in 2018 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Udaipur') & (data['Timestamp'].dt.year == 2018)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Udaipur 2018', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Udaipur') & (data['Timestamp'].dt.year == 2018)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Udaipur 2018', width=450, height=280)


</code>
"
133,spatio_temporal_aggregation,Plot a grouped bar chart of average PM10 by season and year (2018–2021) for all of India.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    def season(m):
        if m in [12,1,2]: return 'Winter'
        elif m in [3,4,5]: return 'Summer'
        elif m in [6,7,8,9]: return 'Monsoon'
        else: return 'Post-Monsoon'
    df = data[data['Timestamp'].dt.year.isin([2018,2019,2020,2021])].copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df['Season'] = df['Timestamp'].dt.month.apply(season)
    df = df.groupby(['Season','Year'])['PM10'].mean().reset_index().dropna()
    order = ['Winter','Summer','Post-Monsoon','Monsoon']
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Season:N', sort=order, title='Season'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('Year:N', title='Year'),
        xOffset='Year:N',
        tooltip=['Season:N','Year:N', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Average PM10 by Season and Year (2018–2021)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     def season(m):
         if m in [12,1,2]: return 'Winter'
         elif m in [3,4,5]: return 'Summer'
         elif m in [6,7,8,9]: return 'Monsoon'
         else: return 'Post-Monsoon'
     df = data[data['Timestamp'].dt.year.isin([2018,2019,2020,2021])].copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df['Season'] = df['Timestamp'].dt.month.apply(season)
     df = df.groupby(['Season','Year'])['PM10'].mean().reset_index().dropna()
     order = ['Winter','Summer','Post-Monsoon','Monsoon']
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Season:N', sort=order, title='Season'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('Year:N', title='Year'),
         xOffset='Year:N',
         tooltip=['Season:N','Year:N', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Average PM10 by Season and Year (2018–2021)', width=500, height=320)
     return chart


</code>
"
134,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Haryana, Gujarat, and Madhya Pradesh across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Haryana', 'Gujarat', 'Madhya Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Haryana', 'Gujarat', 'Madhya Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
135,spatial_aggregation,Show a bar chart of the top 13 cities by median PM2.5 in 2020.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2020]
    df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
    df = df.nlargest(13, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
        y=alt.Y('city:N', sort='-x', title='City'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
        tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 13 Cities by Median PM2.5 in 2020', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2020]
     df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
     df = df.nlargest(13, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
         y=alt.Y('city:N', sort='-x', title='City'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
         tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 13 Cities by Median PM2.5 in 2020', width=500, height=300)
     return chart


</code>
"
136,funding_based,Visualize NCAP city-level funding for FY 2021-22 as a horizontal bar chart for the top 5 cities.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = ncap_funding_data[['city','state','Amount released during FY 2021-22']].copy()
    df.columns = ['city','state','Amount (Cr)']
    df = df.nlargest(5, 'Amount (Cr)')
    df['City_State'] = df['city'] + ', ' + df['state']
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Amount (Cr):Q', title='Amount Released (Cr)'),
        y=alt.Y('City_State:N', sort='-x', title='City, State'),
        color=alt.Color('state:N', title='State'),
        tooltip=['city:N','state:N', alt.Tooltip('Amount (Cr):Q', format='.1f')]
    ).properties(title='Top 5 Cities by NCAP Funding – FY 2021-22', width=500, height=400)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = ncap_funding_data[['city','state','Amount released during FY 2021-22']].copy()
     df.columns = ['city','state','Amount (Cr)']
     df = df.nlargest(5, 'Amount (Cr)')
     df['City_State'] = df['city'] + ', ' + df['state']
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Amount (Cr):Q', title='Amount Released (Cr)'),
         y=alt.Y('City_State:N', sort='-x', title='City, State'),
         color=alt.Color('state:N', title='State'),
         tooltip=['city:N','state:N', alt.Tooltip('Amount (Cr):Q', format='.1f')]
     ).properties(title='Top 5 Cities by NCAP Funding – FY 2021-22', width=500, height=400)
     return chart


</code>
"
137,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Maharashtra, Kerala, and Delhi in 2018 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Maharashtra', 'Kerala', 'Delhi']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2018)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Maharashtra, Kerala, UP – 2018', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Maharashtra', 'Kerala', 'Delhi']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2018)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Maharashtra, Kerala, UP – 2018', width=550, height=320)
     return chart


</code>
"
138,population_based,"Plot a scatter chart of state-level average PM2.5 versus population for 2017, with point size representing area.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    pm = data[data['Timestamp'].dt.year == 2017].groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = pm.merge(states_data, on='state')
    chart = alt.Chart(df).mark_point(filled=True, opacity=0.7).encode(
        x=alt.X('population:Q', title='Population', axis=alt.Axis(format='~s')),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        size=alt.Size('area (km2):Q', title='Area (km²)', scale=alt.Scale(range=[50,1000])),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f'),
                 alt.Tooltip('population:Q', format=','), 'area (km2):Q']
    ).properties(title='PM2.5 vs Population (size=Area) – 2017', width=500, height=400)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     pm = data[data['Timestamp'].dt.year == 2017].groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = pm.merge(states_data, on='state')
     chart = alt.Chart(df).mark_point(filled=True, opacity=0.7).encode(
         x=alt.X('population:Q', title='Population', axis=alt.Axis(format='~s')),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         size=alt.Size('area (km2):Q', title='Area (km²)', scale=alt.Scale(range=[50,1000])),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f'),
                  alt.Tooltip('population:Q', format=','), 'area (km2):Q']
     ).properties(title='PM2.5 vs Population (size=Area) – 2017', width=500, height=400)
     return chart


</code>
"
139,spatial_aggregation,"Create a scatter plot of PM2.5 vs PM10 for all stations in 2019, colored by state.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2019]
    df = df.groupby(['station','state'])[['PM2.5','PM10']].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_point(opacity=0.6, size=60).encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['station:N','state:N',
                 alt.Tooltip('PM2\.5:Q', format='.1f'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='PM2.5 vs PM10 by Station – 2019', width=500, height=400)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2019]
     df = df.groupby(['station','state'])[['PM2.5','PM10']].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_point(opacity=0.6, size=60).encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['station:N','state:N',
                  alt.Tooltip('PM2\.5:Q', format='.1f'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='PM2.5 vs PM10 by Station – 2019', width=500, height=400)
     return chart


</code>
"
140,spatio_temporal_aggregation,Create a grouped bar chart comparing the average PM2.5 in Winter vs Summer for the top 9 most polluted states.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    top6 = data.groupby('state')['PM2.5'].mean().nlargest(9).index.tolist()
    df = data[data['state'].isin(top6)].copy()
    df['Season'] = df['Timestamp'].dt.month.apply(
        lambda m: 'Winter' if m in [12,1,2] else ('Summer' if m in [3,4,5] else None))
    df = df.dropna(subset=['Season'])
    df = df.groupby(['state','Season'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('state:N', title='State'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('Season:N', title='Season'),
        xOffset='Season:N',
        tooltip=['state:N','Season:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Winter vs Summer PM2.5 – Top 9 Polluted States', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     top6 = data.groupby('state')['PM2.5'].mean().nlargest(9).index.tolist()
     df = data[data['state'].isin(top6)].copy()
     df['Season'] = df['Timestamp'].dt.month.apply(
         lambda m: 'Winter' if m in [12,1,2] else ('Summer' if m in [3,4,5] else None))
     df = df.dropna(subset=['Season'])
     df = df.groupby(['state','Season'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('state:N', title='State'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('Season:N', title='Season'),
         xOffset='Season:N',
         tooltip=['state:N','Season:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Winter vs Summer PM2.5 – Top 9 Polluted States', width=550, height=320)
     return chart


</code>
"
141,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Chandigarh, Himachal Pradesh, and Delhi across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Chandigarh', 'Himachal Pradesh', 'Delhi']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Chandigarh', 'Himachal Pradesh', 'Delhi']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
142,spatio_temporal_aggregation,Create a heatmap showing the average PM2.5 by month (x-axis) and year (y-axis) for Arunachal Pradesh.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Arunachal Pradesh'].copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_rect().encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Year:N', title='Year'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='Avg PM2.5'),
        tooltip=['Year:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Heatmap – Arunachal Pradesh (Month × Year)', width=500, height=280)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Arunachal Pradesh'].copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_rect().encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Year:N', title='Year'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='Avg PM2.5'),
         tooltip=['Year:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Heatmap – Arunachal Pradesh (Month × Year)', width=500, height=280)
     return chart


</code>
"
143,spatial_aggregation,Plot the average PM2.5 across all states in February 2022 as a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 2)]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('PM2.5', ascending=False)
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='plasma'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Average PM2.5 by State – February 2022', width=500, height=400)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['Timestamp'].dt.year == 2022) & (data['Timestamp'].dt.month == 2)]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('PM2.5', ascending=False)
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='plasma'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Average PM2.5 by State – February 2022', width=500, height=400)
     return chart


</code>
"
144,funding_based,Show a scatter plot of total NCAP funding vs average PM2.5 (2018) per state.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    pm = data[data['Timestamp'].dt.year == 2018].groupby('state')['PM2.5'].mean().reset_index()
    funding = ncap_funding_data.groupby('state')['Total fund released'].sum().reset_index()
    df = pm.merge(funding, on='state').dropna()
    chart = alt.Chart(df).mark_point(filled=True, size=100).encode(
        x=alt.X('Total fund released:Q', title='Total NCAP Funding (Cr)'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 – 2018 (µg/m³)'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f'),
                 alt.Tooltip('Total fund released:Q', format='.1f')]
    ).properties(title='NCAP Funding vs Average PM2.5 by State (2018)', width=450, height=350)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     pm = data[data['Timestamp'].dt.year == 2018].groupby('state')['PM2.5'].mean().reset_index()
     funding = ncap_funding_data.groupby('state')['Total fund released'].sum().reset_index()
     df = pm.merge(funding, on='state').dropna()
     chart = alt.Chart(df).mark_point(filled=True, size=100).encode(
         x=alt.X('Total fund released:Q', title='Total NCAP Funding (Cr)'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 – 2018 (µg/m³)'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f'),
                  alt.Tooltip('Total fund released:Q', format='.1f')]
     ).properties(title='NCAP Funding vs Average PM2.5 by State (2018)', width=450, height=350)
     return chart


</code>
"
145,spatial_aggregation,Show a bar chart of the top 6 cities by median PM2.5 in 2021.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2021]
    df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
    df = df.nlargest(6, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
        y=alt.Y('city:N', sort='-x', title='City'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
        tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 6 Cities by Median PM2.5 in 2021', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2021]
     df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
     df = df.nlargest(6, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
         y=alt.Y('city:N', sort='-x', title='City'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
         tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 6 Cities by Median PM2.5 in 2021', width=500, height=300)
     return chart


</code>
"
146,specific_pattern,Plot the rolling 30-day average PM2.5 for Gujarat in 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Gujarat') & (data['Timestamp'].dt.year == 2024)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Gujarat 2024', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Gujarat') & (data['Timestamp'].dt.year == 2024)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Gujarat 2024', width=600, height=300)


</code>
"
147,area_based,Show the PM2.5 per 1000 km² (air quality density) for each state in 2018 as a bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    pm = data[data['Timestamp'].dt.year == 2018].groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = pm.merge(states_data[['state','area (km2)']], on='state')
    df['PM2.5 per 1000 km²'] = df['PM2.5'] / df['area (km2)'] * 1000
    df = df.sort_values('PM2.5 per 1000 km²', ascending=False)
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5 per 1000 km²:Q', title='PM2.5 per 1000 km²'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM2\.5 per 1000 km²:Q', scale=alt.Scale(scheme='orangered'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5 per 1000 km²:Q', format='.3f')]
    ).properties(title='Air Quality Density (PM2.5 per 1000 km²) by State – 2018', width=500, height=400)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     pm = data[data['Timestamp'].dt.year == 2018].groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = pm.merge(states_data[['state','area (km2)']], on='state')
     df['PM2.5 per 1000 km²'] = df['PM2.5'] / df['area (km2)'] * 1000
     df = df.sort_values('PM2.5 per 1000 km²', ascending=False)
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5 per 1000 km²:Q', title='PM2.5 per 1000 km²'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM2\.5 per 1000 km²:Q', scale=alt.Scale(scheme='orangered'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5 per 1000 km²:Q', format='.3f')]
     ).properties(title='Air Quality Density (PM2.5 per 1000 km²) by State – 2018', width=500, height=400)
     return chart


</code>
"
148,spatial_aggregation,Visualize the bottom 6 states with the lowest average PM2.5 in 2022 using a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2022]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nsmallest(6, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Bottom 6 States by Average PM2.5 in 2022', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2022]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nsmallest(6, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Bottom 6 States by Average PM2.5 in 2022', width=500, height=300)
     return chart


</code>
"
149,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Mizoram stations in 2023, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Mizoram') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Mizoram Stations 2023', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Mizoram') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Mizoram Stations 2023', width=450, height=350)


</code>
"
150,area_based,Show the PM2.5 per 1000 km² (air quality density) for each state in 2022 as a bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    pm = data[data['Timestamp'].dt.year == 2022].groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = pm.merge(states_data[['state','area (km2)']], on='state')
    df['PM2.5 per 1000 km²'] = df['PM2.5'] / df['area (km2)'] * 1000
    df = df.sort_values('PM2.5 per 1000 km²', ascending=False)
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5 per 1000 km²:Q', title='PM2.5 per 1000 km²'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM2\.5 per 1000 km²:Q', scale=alt.Scale(scheme='orangered'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5 per 1000 km²:Q', format='.3f')]
    ).properties(title='Air Quality Density (PM2.5 per 1000 km²) by State – 2022', width=500, height=400)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     pm = data[data['Timestamp'].dt.year == 2022].groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = pm.merge(states_data[['state','area (km2)']], on='state')
     df['PM2.5 per 1000 km²'] = df['PM2.5'] / df['area (km2)'] * 1000
     df = df.sort_values('PM2.5 per 1000 km²', ascending=False)
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5 per 1000 km²:Q', title='PM2.5 per 1000 km²'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM2\.5 per 1000 km²:Q', scale=alt.Scale(scheme='orangered'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5 per 1000 km²:Q', format='.3f')]
     ).properties(title='Air Quality Density (PM2.5 per 1000 km²) by State – 2022', width=500, height=400)
     return chart


</code>
"
151,spatio_temporal_aggregation,Create a heatmap showing the average PM2.5 by month (x-axis) and year (y-axis) for Uttarakhand.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Uttarakhand'].copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_rect().encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Year:N', title='Year'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='Avg PM2.5'),
        tooltip=['Year:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Heatmap – Uttarakhand (Month × Year)', width=500, height=280)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Uttarakhand'].copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_rect().encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Year:N', title='Year'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='Avg PM2.5'),
         tooltip=['Year:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Heatmap – Uttarakhand (Month × Year)', width=500, height=280)
     return chart


</code>
"
152,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Kaithal, Jaipur, and Kota in 2018 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Kaithal', 'Jaipur', 'Kota']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2018)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Kaithal vs Jaipur vs Kota – 2018', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Kaithal', 'Jaipur', 'Kota']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2018)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Kaithal vs Jaipur vs Kota – 2018', width=550, height=320)
     return chart


</code>
"
153,population_based,"Plot a scatter chart of state-level average PM2.5 versus population for 2018, with point size representing area.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    pm = data[data['Timestamp'].dt.year == 2018].groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = pm.merge(states_data, on='state')
    chart = alt.Chart(df).mark_point(filled=True, opacity=0.7).encode(
        x=alt.X('population:Q', title='Population', axis=alt.Axis(format='~s')),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        size=alt.Size('area (km2):Q', title='Area (km²)', scale=alt.Scale(range=[50,1000])),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f'),
                 alt.Tooltip('population:Q', format=','), 'area (km2):Q']
    ).properties(title='PM2.5 vs Population (size=Area) – 2018', width=500, height=400)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     pm = data[data['Timestamp'].dt.year == 2018].groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = pm.merge(states_data, on='state')
     chart = alt.Chart(df).mark_point(filled=True, opacity=0.7).encode(
         x=alt.X('population:Q', title='Population', axis=alt.Axis(format='~s')),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         size=alt.Size('area (km2):Q', title='Area (km²)', scale=alt.Scale(range=[50,1000])),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f'),
                  alt.Tooltip('population:Q', format=','), 'area (km2):Q']
     ).properties(title='PM2.5 vs Population (size=Area) – 2018', width=500, height=400)
     return chart


</code>
"
154,specific_pattern,Show PM2.5 exceedances above the Indian standard (60 µg/m³) per year as a bar chart for Odisha.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Odisha'].dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 60].copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby('Year')['Timestamp'].nunique().reset_index()
    df.columns = ['Year','Days Exceeded']
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('Days Exceeded:Q', title='Days PM2.5 > 60 µg/m³'),
        tooltip=['Year:N','Days Exceeded:Q']
    ).properties(title='Days Odisha Exceeded Indian PM2.5 Standard (60 µg/m³) per Year', width=450, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Odisha'].dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 60].copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby('Year')['Timestamp'].nunique().reset_index()
     df.columns = ['Year','Days Exceeded']
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('Days Exceeded:Q', title='Days PM2.5 > 60 µg/m³'),
         tooltip=['Year:N','Days Exceeded:Q']
     ).properties(title='Days Odisha Exceeded Indian PM2.5 Standard (60 µg/m³) per Year', width=450, height=300)
     return chart


</code>
"
155,spatio_temporal_aggregation,"Create a faceted bar chart showing top 10 states by average PM2.5 per year for 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year.isin([2021,2022,2023,2024])].copy()
    df['Year'] = df['Timestamp'].dt.year
    agg = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    top = agg.groupby('Year').apply(lambda x: x.nlargest(10,'PM2.5')).reset_index(drop=True)
    chart = alt.Chart(top).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Avg PM2.5'),
        y=alt.Y('state:N', sort='-x'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).facet(facet='Year:O', columns=2).properties(title='Top 10 States by PM2.5 per Year')
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year.isin([2021,2022,2023,2024])].copy()
     df['Year'] = df['Timestamp'].dt.year
     agg = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     top = agg.groupby('Year').apply(lambda x: x.nlargest(10,'PM2.5')).reset_index(drop=True)
     chart = alt.Chart(top).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Avg PM2.5'),
         y=alt.Y('state:N', sort='-x'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).facet(facet='Year:O', columns=2).properties(title='Top 10 States by PM2.5 per Year')
     return chart


</code>
"
156,spatial_aggregation,Show a bar chart of the top 12 cities by median PM2.5 in 2017.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2017]
    df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
    df = df.nlargest(12, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
        y=alt.Y('city:N', sort='-x', title='City'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
        tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 12 Cities by Median PM2.5 in 2017', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2017]
     df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
     df = df.nlargest(12, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
         y=alt.Y('city:N', sort='-x', title='City'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
         tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 12 Cities by Median PM2.5 in 2017', width=500, height=300)
     return chart


</code>
"
157,spatial_aggregation,Show a box plot of PM2.5 distribution for each state in 2022.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2022].dropna(subset=['PM2.5'])
    df = df[['state','PM2.5']]
    state_order = df.groupby('state')['PM2.5'].median().sort_values(ascending=False).index.tolist()
    chart = alt.Chart(df).mark_boxplot(extent='min-max').encode(
        x=alt.X('PM2\.5:Q', title='PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort=state_order, title='State'),
        color=alt.Color('state:N', legend=None)
    ).properties(title='PM2.5 Distribution by State – 2022', width=500, height=450)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2022].dropna(subset=['PM2.5'])
     df = df[['state','PM2.5']]
     state_order = df.groupby('state')['PM2.5'].median().sort_values(ascending=False).index.tolist()
     chart = alt.Chart(df).mark_boxplot(extent='min-max').encode(
         x=alt.X('PM2\.5:Q', title='PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort=state_order, title='State'),
         color=alt.Color('state:N', legend=None)
     ).properties(title='PM2.5 Distribution by State – 2022', width=500, height=450)
     return chart


</code>
"
158,spatio_temporal_aggregation,Show a heatmap of average PM2.5 for the top 7 most polluted states by month for 2019.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    top8 = data.groupby('state')['PM2.5'].mean().nlargest(7).index.tolist()
    df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_rect().encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('state:N', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
        tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Heatmap – Top 7 Polluted States by Month (2019)', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     top8 = data.groupby('state')['PM2.5'].mean().nlargest(7).index.tolist()
     df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_rect().encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('state:N', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
         tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Heatmap – Top 7 Polluted States by Month (2019)', width=500, height=300)
     return chart


</code>
"
159,spatio_temporal_aggregation,Plot a grouped bar chart of average PM10 by season and year (2020–2023) for all of India.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    def season(m):
        if m in [12,1,2]: return 'Winter'
        elif m in [3,4,5]: return 'Summer'
        elif m in [6,7,8,9]: return 'Monsoon'
        else: return 'Post-Monsoon'
    df = data[data['Timestamp'].dt.year.isin([2020,2021,2022,2023])].copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df['Season'] = df['Timestamp'].dt.month.apply(season)
    df = df.groupby(['Season','Year'])['PM10'].mean().reset_index().dropna()
    order = ['Winter','Summer','Post-Monsoon','Monsoon']
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Season:N', sort=order, title='Season'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('Year:N', title='Year'),
        xOffset='Year:N',
        tooltip=['Season:N','Year:N', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Average PM10 by Season and Year (2020–2023)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     def season(m):
         if m in [12,1,2]: return 'Winter'
         elif m in [3,4,5]: return 'Summer'
         elif m in [6,7,8,9]: return 'Monsoon'
         else: return 'Post-Monsoon'
     df = data[data['Timestamp'].dt.year.isin([2020,2021,2022,2023])].copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df['Season'] = df['Timestamp'].dt.month.apply(season)
     df = df.groupby(['Season','Year'])['PM10'].mean().reset_index().dropna()
     order = ['Winter','Summer','Post-Monsoon','Monsoon']
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Season:N', sort=order, title='Season'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('Year:N', title='Year'),
         xOffset='Year:N',
         tooltip=['Season:N','Year:N', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Average PM10 by Season and Year (2020–2023)', width=500, height=320)
     return chart


</code>
"
160,spatial_aggregation,Show a box plot of PM2.5 distribution for each state in 2024.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2024].dropna(subset=['PM2.5'])
    df = df[['state','PM2.5']]
    state_order = df.groupby('state')['PM2.5'].median().sort_values(ascending=False).index.tolist()
    chart = alt.Chart(df).mark_boxplot(extent='min-max').encode(
        x=alt.X('PM2\.5:Q', title='PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort=state_order, title='State'),
        color=alt.Color('state:N', legend=None)
    ).properties(title='PM2.5 Distribution by State – 2024', width=500, height=450)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2024].dropna(subset=['PM2.5'])
     df = df[['state','PM2.5']]
     state_order = df.groupby('state')['PM2.5'].median().sort_values(ascending=False).index.tolist()
     chart = alt.Chart(df).mark_boxplot(extent='min-max').encode(
         x=alt.X('PM2\.5:Q', title='PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort=state_order, title='State'),
         color=alt.Color('state:N', legend=None)
     ).properties(title='PM2.5 Distribution by State – 2024', width=500, height=450)
     return chart


</code>
"
161,funding_based,Show a scatter plot of total NCAP funding vs average PM2.5 (2017) per state.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    pm = data[data['Timestamp'].dt.year == 2017].groupby('state')['PM2.5'].mean().reset_index()
    funding = ncap_funding_data.groupby('state')['Total fund released'].sum().reset_index()
    df = pm.merge(funding, on='state').dropna()
    chart = alt.Chart(df).mark_point(filled=True, size=100).encode(
        x=alt.X('Total fund released:Q', title='Total NCAP Funding (Cr)'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 – 2017 (µg/m³)'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f'),
                 alt.Tooltip('Total fund released:Q', format='.1f')]
    ).properties(title='NCAP Funding vs Average PM2.5 by State (2017)', width=450, height=350)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     pm = data[data['Timestamp'].dt.year == 2017].groupby('state')['PM2.5'].mean().reset_index()
     funding = ncap_funding_data.groupby('state')['Total fund released'].sum().reset_index()
     df = pm.merge(funding, on='state').dropna()
     chart = alt.Chart(df).mark_point(filled=True, size=100).encode(
         x=alt.X('Total fund released:Q', title='Total NCAP Funding (Cr)'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 – 2017 (µg/m³)'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f'),
                  alt.Tooltip('Total fund released:Q', format='.1f')]
     ).properties(title='NCAP Funding vs Average PM2.5 by State (2017)', width=450, height=350)
     return chart


</code>
"
162,specific_pattern,Show PM2.5 exceedances above the Indian standard (60 µg/m³) per year as a bar chart for Telangana.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Telangana'].dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 60].copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby('Year')['Timestamp'].nunique().reset_index()
    df.columns = ['Year','Days Exceeded']
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('Days Exceeded:Q', title='Days PM2.5 > 60 µg/m³'),
        tooltip=['Year:N','Days Exceeded:Q']
    ).properties(title='Days Telangana Exceeded Indian PM2.5 Standard (60 µg/m³) per Year', width=450, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Telangana'].dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 60].copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby('Year')['Timestamp'].nunique().reset_index()
     df.columns = ['Year','Days Exceeded']
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('Days Exceeded:Q', title='Days PM2.5 > 60 µg/m³'),
         tooltip=['Year:N','Days Exceeded:Q']
     ).properties(title='Days Telangana Exceeded Indian PM2.5 Standard (60 µg/m³) per Year', width=450, height=300)
     return chart


</code>
"
163,population_based,"Plot average PM2.5 (2019) vs state population as a scatter plot, labeling each point with the state name.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    pm = data[data['Timestamp'].dt.year == 2019].groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = pm.merge(states_data, on='state')
    points = alt.Chart(df).mark_point(filled=True, size=80).encode(
        x=alt.X('population:Q', title='Population', axis=alt.Axis(format='~s')),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('population:Q', format=',')]
    )
    labels = alt.Chart(df).mark_text(align='left', dx=5, fontSize=9).encode(
        x='population:Q', y='PM2\.5:Q', text='state:N'
    )
    return (points + labels).properties(title='PM2.5 vs Population by State – 2019', width=500, height=400)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     pm = data[data['Timestamp'].dt.year == 2019].groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = pm.merge(states_data, on='state')
     points = alt.Chart(df).mark_point(filled=True, size=80).encode(
         x=alt.X('population:Q', title='Population', axis=alt.Axis(format='~s')),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('population:Q', format=',')]
     )
     labels = alt.Chart(df).mark_text(align='left', dx=5, fontSize=9).encode(
         x='population:Q', y='PM2\.5:Q', text='state:N'
     )
     return (points + labels).properties(title='PM2.5 vs Population by State – 2019', width=500, height=400)


</code>
"
164,specific_pattern,Show PM2.5 exceedances above the Indian standard (60 µg/m³) per year as a bar chart for Bihar.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Bihar'].dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 60].copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby('Year')['Timestamp'].nunique().reset_index()
    df.columns = ['Year','Days Exceeded']
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('Days Exceeded:Q', title='Days PM2.5 > 60 µg/m³'),
        tooltip=['Year:N','Days Exceeded:Q']
    ).properties(title='Days Bihar Exceeded Indian PM2.5 Standard (60 µg/m³) per Year', width=450, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Bihar'].dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 60].copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby('Year')['Timestamp'].nunique().reset_index()
     df.columns = ['Year','Days Exceeded']
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('Days Exceeded:Q', title='Days PM2.5 > 60 µg/m³'),
         tooltip=['Year:N','Days Exceeded:Q']
     ).properties(title='Days Bihar Exceeded Indian PM2.5 Standard (60 µg/m³) per Year', width=450, height=300)
     return chart


</code>
"
165,temporal_aggregation,Show the monthly average PM10 trend for Byasanagar from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Byasanagar'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Byasanagar (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Byasanagar'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Byasanagar (2019–2024)', width=600, height=300)
     return chart


</code>
"
166,spatial_aggregation,Show a bar chart comparing the 75th percentile PM2.5 of all states in winter 2023 (November–February).,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.month.isin([11,12,1,2])]
    df = df[df['Timestamp'].dt.year.isin([2023,2024])]
    df = df.groupby('state')['PM2.5'].quantile(0.75).reset_index().dropna()
    df.columns = ['state','PM2.5_p75']
    df = df.sort_values('PM2.5_p75', ascending=False)
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5_p75:Q', title='75th Percentile PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM2\.5_p75:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5_p75:Q', format='.1f', title='P75 PM2.5')]
    ).properties(title='75th Percentile PM2.5 by State – Winter 2023', width=500, height=400)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.month.isin([11,12,1,2])]
     df = df[df['Timestamp'].dt.year.isin([2023,2024])]
     df = df.groupby('state')['PM2.5'].quantile(0.75).reset_index().dropna()
     df.columns = ['state','PM2.5_p75']
     df = df.sort_values('PM2.5_p75', ascending=False)
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5_p75:Q', title='75th Percentile PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM2\.5_p75:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5_p75:Q', format='.1f', title='P75 PM2.5')]
     ).properties(title='75th Percentile PM2.5 by State – Winter 2023', width=500, height=400)
     return chart


</code>
"
167,spatio_temporal_aggregation,Plot the yearly average PM2.5 for the top 9 most polluted states from 2017 to 2024 as a multi-line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    top5 = data.groupby('state')['PM2.5'].mean().nlargest(9).index.tolist()
    df = data[data['state'].isin(top5)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends – Top 9 Most Polluted States', width=600, height=350)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     top5 = data.groupby('state')['PM2.5'].mean().nlargest(9).index.tolist()
     df = data[data['state'].isin(top5)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends – Top 9 Most Polluted States', width=600, height=350)
     return chart


</code>
"
168,spatio_temporal_aggregation,Plot a heatmap of average PM10 by state (y-axis) and month (x-axis) for 2018.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2018].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['state','Month'])['PM10'].mean().reset_index().dropna()
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_rect().encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('state:N', title='State'),
        color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), title='Avg PM10'),
        tooltip=['state:N','MonthName:N', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='PM10 Heatmap by State and Month – 2018', width=500, height=400)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2018].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['state','Month'])['PM10'].mean().reset_index().dropna()
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_rect().encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('state:N', title='State'),
         color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), title='Avg PM10'),
         tooltip=['state:N','MonthName:N', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='PM10 Heatmap by State and Month – 2018', width=500, height=400)
     return chart


</code>
"
169,specific_pattern,Plot the rolling 30-day average PM2.5 for Tripura in 2023 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Tripura') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Tripura 2023', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Tripura') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Tripura 2023', width=600, height=300)


</code>
"
170,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Ballabgarh, Jhalawar, and Sirohi in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Ballabgarh', 'Jhalawar', 'Sirohi']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Ballabgarh vs Jhalawar vs Sirohi – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Ballabgarh', 'Jhalawar', 'Sirohi']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Ballabgarh vs Jhalawar vs Sirohi – 2022', width=550, height=320)
     return chart


</code>
"
171,spatial_aggregation,Plot the median PM10 for each state in summer 2023 (April–June) as a sorted bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['Timestamp'].dt.year == 2023) & (data['Timestamp'].dt.month.isin([4,5,6]))]
    df = df.groupby('state')['PM10'].median().reset_index().dropna()
    df = df.sort_values('PM10', ascending=False)
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM10:Q', title='Median PM10 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM10:Q', scale=alt.Scale(scheme='yelloworangered'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Median PM10 by State – Summer 2023 (Apr–Jun)', width=500, height=400)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['Timestamp'].dt.year == 2023) & (data['Timestamp'].dt.month.isin([4,5,6]))]
     df = df.groupby('state')['PM10'].median().reset_index().dropna()
     df = df.sort_values('PM10', ascending=False)
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM10:Q', title='Median PM10 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM10:Q', scale=alt.Scale(scheme='yelloworangered'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Median PM10 by State – Summer 2023 (Apr–Jun)', width=500, height=400)
     return chart


</code>
"
172,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Arunachal Pradesh, Jammu and Kashmir, and Andhra Pradesh across 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Arunachal Pradesh', 'Jammu and Kashmir', 'Andhra Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Arunachal Pradesh', 'Jammu and Kashmir', 'Andhra Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
     return chart


</code>
"
173,temporal_aggregation,Show the monthly average PM10 trend for Delhi from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Delhi'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Delhi (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Delhi'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Delhi (2019–2024)', width=600, height=300)
     return chart


</code>
"
174,spatio_temporal_aggregation,Plot a grouped bar chart of average PM10 by season and year (2021–2024) for all of India.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    def season(m):
        if m in [12,1,2]: return 'Winter'
        elif m in [3,4,5]: return 'Summer'
        elif m in [6,7,8,9]: return 'Monsoon'
        else: return 'Post-Monsoon'
    df = data[data['Timestamp'].dt.year.isin([2021,2022,2023,2024])].copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df['Season'] = df['Timestamp'].dt.month.apply(season)
    df = df.groupby(['Season','Year'])['PM10'].mean().reset_index().dropna()
    order = ['Winter','Summer','Post-Monsoon','Monsoon']
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Season:N', sort=order, title='Season'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('Year:N', title='Year'),
        xOffset='Year:N',
        tooltip=['Season:N','Year:N', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Average PM10 by Season and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     def season(m):
         if m in [12,1,2]: return 'Winter'
         elif m in [3,4,5]: return 'Summer'
         elif m in [6,7,8,9]: return 'Monsoon'
         else: return 'Post-Monsoon'
     df = data[data['Timestamp'].dt.year.isin([2021,2022,2023,2024])].copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df['Season'] = df['Timestamp'].dt.month.apply(season)
     df = df.groupby(['Season','Year'])['PM10'].mean().reset_index().dropna()
     order = ['Winter','Summer','Post-Monsoon','Monsoon']
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Season:N', sort=order, title='Season'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('Year:N', title='Year'),
         xOffset='Year:N',
         tooltip=['Season:N','Year:N', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Average PM10 by Season and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
175,population_based,"Plot average PM2.5 (2017) vs state population as a scatter plot, labeling each point with the state name.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    pm = data[data['Timestamp'].dt.year == 2017].groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = pm.merge(states_data, on='state')
    points = alt.Chart(df).mark_point(filled=True, size=80).encode(
        x=alt.X('population:Q', title='Population', axis=alt.Axis(format='~s')),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('population:Q', format=',')]
    )
    labels = alt.Chart(df).mark_text(align='left', dx=5, fontSize=9).encode(
        x='population:Q', y='PM2\.5:Q', text='state:N'
    )
    return (points + labels).properties(title='PM2.5 vs Population by State – 2017', width=500, height=400)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     pm = data[data['Timestamp'].dt.year == 2017].groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = pm.merge(states_data, on='state')
     points = alt.Chart(df).mark_point(filled=True, size=80).encode(
         x=alt.X('population:Q', title='Population', axis=alt.Axis(format='~s')),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('population:Q', format=',')]
     )
     labels = alt.Chart(df).mark_text(align='left', dx=5, fontSize=9).encode(
         x='population:Q', y='PM2\.5:Q', text='state:N'
     )
     return (points + labels).properties(title='PM2.5 vs Population by State – 2017', width=500, height=400)


</code>
"
176,temporal_aggregation,Plot the monthly average PM2.5 trend for Assam from 2017 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Assam'].copy()
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='steelblue').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly Average PM2.5 Trend – Assam (2017–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Assam'].copy()
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='steelblue').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly Average PM2.5 Trend – Assam (2017–2024)', width=600, height=300)
     return chart


</code>
"
177,specific_pattern,Show a cumulative area chart of PM2.5 readings for Varanasi across 2018.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Varanasi') & (data['Timestamp'].dt.year == 2018)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Varanasi 2018', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Varanasi') & (data['Timestamp'].dt.year == 2018)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Varanasi 2018', width=600, height=300)
     return chart


</code>
"
178,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Arunachal Pradesh, Jharkhand, and Himachal Pradesh across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Arunachal Pradesh', 'Jharkhand', 'Himachal Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Arunachal Pradesh', 'Jharkhand', 'Himachal Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
179,spatial_aggregation,Visualize the bottom 8 states with the lowest average PM2.5 in 2019 using a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2019]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nsmallest(8, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Bottom 8 States by Average PM2.5 in 2019', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2019]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nsmallest(8, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Bottom 8 States by Average PM2.5 in 2019', width=500, height=300)
     return chart


</code>
"
180,specific_pattern,Plot the rolling 30-day average PM2.5 for Puducherry in 2018 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Puducherry') & (data['Timestamp'].dt.year == 2018)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Puducherry 2018', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Puducherry') & (data['Timestamp'].dt.year == 2018)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Puducherry 2018', width=600, height=300)


</code>
"
181,spatial_aggregation,Show a bar chart comparing the 75th percentile PM2.5 of all states in winter 2019 (November–February).,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.month.isin([11,12,1,2])]
    df = df[df['Timestamp'].dt.year.isin([2019,2020])]
    df = df.groupby('state')['PM2.5'].quantile(0.75).reset_index().dropna()
    df.columns = ['state','PM2.5_p75']
    df = df.sort_values('PM2.5_p75', ascending=False)
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5_p75:Q', title='75th Percentile PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM2\.5_p75:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5_p75:Q', format='.1f', title='P75 PM2.5')]
    ).properties(title='75th Percentile PM2.5 by State – Winter 2019', width=500, height=400)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.month.isin([11,12,1,2])]
     df = df[df['Timestamp'].dt.year.isin([2019,2020])]
     df = df.groupby('state')['PM2.5'].quantile(0.75).reset_index().dropna()
     df.columns = ['state','PM2.5_p75']
     df = df.sort_values('PM2.5_p75', ascending=False)
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5_p75:Q', title='75th Percentile PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM2\.5_p75:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5_p75:Q', format='.1f', title='P75 PM2.5')]
     ).properties(title='75th Percentile PM2.5 by State – Winter 2019', width=500, height=400)
     return chart


</code>
"
182,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Rajasthan stations in 2021, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Rajasthan') & (data['Timestamp'].dt.year == 2021)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Rajasthan Stations 2021', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Rajasthan') & (data['Timestamp'].dt.year == 2021)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Rajasthan Stations 2021', width=450, height=350)


</code>
"
183,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Tamil Nadu, Chhattisgarh, and Uttar Pradesh in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Tamil Nadu', 'Chhattisgarh', 'Uttar Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Tamil Nadu, Chhattisgarh, UP – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Tamil Nadu', 'Chhattisgarh', 'Uttar Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Tamil Nadu, Chhattisgarh, UP – 2024', width=550, height=320)
     return chart


</code>
"
184,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Chhattisgarh stations in 2017, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Chhattisgarh') & (data['Timestamp'].dt.year == 2017)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Chhattisgarh Stations 2017', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Chhattisgarh') & (data['Timestamp'].dt.year == 2017)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Chhattisgarh Stations 2017', width=450, height=350)


</code>
"
185,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Puducherry stations in 2024, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Puducherry') & (data['Timestamp'].dt.year == 2024)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Puducherry Stations 2024', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Puducherry') & (data['Timestamp'].dt.year == 2024)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Puducherry Stations 2024', width=450, height=350)


</code>
"
186,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Tamil Nadu, Chhattisgarh, and Punjab in 2019 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Tamil Nadu', 'Chhattisgarh', 'Punjab']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Tamil Nadu, Chhattisgarh, UP – 2019', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Tamil Nadu', 'Chhattisgarh', 'Punjab']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Tamil Nadu, Chhattisgarh, UP – 2019', width=550, height=320)
     return chart


</code>
"
187,spatial_aggregation,Show a bar chart comparing the 75th percentile PM2.5 of all states in winter 2020 (November–February).,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.month.isin([11,12,1,2])]
    df = df[df['Timestamp'].dt.year.isin([2020,2021])]
    df = df.groupby('state')['PM2.5'].quantile(0.75).reset_index().dropna()
    df.columns = ['state','PM2.5_p75']
    df = df.sort_values('PM2.5_p75', ascending=False)
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5_p75:Q', title='75th Percentile PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM2\.5_p75:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5_p75:Q', format='.1f', title='P75 PM2.5')]
    ).properties(title='75th Percentile PM2.5 by State – Winter 2020', width=500, height=400)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.month.isin([11,12,1,2])]
     df = df[df['Timestamp'].dt.year.isin([2020,2021])]
     df = df.groupby('state')['PM2.5'].quantile(0.75).reset_index().dropna()
     df.columns = ['state','PM2.5_p75']
     df = df.sort_values('PM2.5_p75', ascending=False)
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5_p75:Q', title='75th Percentile PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM2\.5_p75:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5_p75:Q', format='.1f', title='P75 PM2.5')]
     ).properties(title='75th Percentile PM2.5 by State – Winter 2020', width=500, height=400)
     return chart


</code>
"
188,spatial_aggregation,Visualize the bottom 12 states with the lowest average PM2.5 in 2024 using a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2024]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nsmallest(12, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Bottom 12 States by Average PM2.5 in 2024', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2024]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nsmallest(12, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Bottom 12 States by Average PM2.5 in 2024', width=500, height=300)
     return chart


</code>
"
189,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Delhi, Uttarakhand, and Telangana from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Delhi', 'Uttarakhand', 'Telangana']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Delhi vs Uttarakhand vs Telangana', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Delhi', 'Uttarakhand', 'Telangana']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Delhi vs Uttarakhand vs Telangana', width=550, height=320)
     return chart


</code>
"
190,spatio_temporal_aggregation,Create a grouped bar chart comparing the average PM2.5 in Winter vs Summer for the top 10 most polluted states.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    top6 = data.groupby('state')['PM2.5'].mean().nlargest(10).index.tolist()
    df = data[data['state'].isin(top6)].copy()
    df['Season'] = df['Timestamp'].dt.month.apply(
        lambda m: 'Winter' if m in [12,1,2] else ('Summer' if m in [3,4,5] else None))
    df = df.dropna(subset=['Season'])
    df = df.groupby(['state','Season'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('state:N', title='State'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('Season:N', title='Season'),
        xOffset='Season:N',
        tooltip=['state:N','Season:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Winter vs Summer PM2.5 – Top 10 Polluted States', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     top6 = data.groupby('state')['PM2.5'].mean().nlargest(10).index.tolist()
     df = data[data['state'].isin(top6)].copy()
     df['Season'] = df['Timestamp'].dt.month.apply(
         lambda m: 'Winter' if m in [12,1,2] else ('Summer' if m in [3,4,5] else None))
     df = df.dropna(subset=['Season'])
     df = df.groupby(['state','Season'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('state:N', title='State'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('Season:N', title='Season'),
         xOffset='Season:N',
         tooltip=['state:N','Season:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Winter vs Summer PM2.5 – Top 10 Polluted States', width=550, height=320)
     return chart


</code>
"
191,spatio_temporal_aggregation,"Create a faceted bar chart showing top 8 states by average PM2.5 per year for 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year.isin([2019,2020,2021,2022])].copy()
    df['Year'] = df['Timestamp'].dt.year
    agg = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    top = agg.groupby('Year').apply(lambda x: x.nlargest(8,'PM2.5')).reset_index(drop=True)
    chart = alt.Chart(top).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Avg PM2.5'),
        y=alt.Y('state:N', sort='-x'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).facet(facet='Year:O', columns=2).properties(title='Top 8 States by PM2.5 per Year')
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year.isin([2019,2020,2021,2022])].copy()
     df['Year'] = df['Timestamp'].dt.year
     agg = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     top = agg.groupby('Year').apply(lambda x: x.nlargest(8,'PM2.5')).reset_index(drop=True)
     chart = alt.Chart(top).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Avg PM2.5'),
         y=alt.Y('state:N', sort='-x'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).facet(facet='Year:O', columns=2).properties(title='Top 8 States by PM2.5 per Year')
     return chart


</code>
"
192,spatial_aggregation,"Show the top 8 states by average PM10 in 2023 as a bar chart, sorted in descending order.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2023]
    df = df.groupby('state')['PM10'].mean().reset_index().dropna()
    df = df.nlargest(8, 'PM10')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Top 8 States by Average PM10 in 2023', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2023]
     df = df.groupby('state')['PM10'].mean().reset_index().dropna()
     df = df.nlargest(8, 'PM10')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Top 8 States by Average PM10 in 2023', width=500, height=300)
     return chart


</code>
"
193,spatio_temporal_aggregation,Plot the yearly average PM2.5 for the top 6 most polluted states from 2017 to 2024 as a multi-line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    top5 = data.groupby('state')['PM2.5'].mean().nlargest(6).index.tolist()
    df = data[data['state'].isin(top5)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends – Top 6 Most Polluted States', width=600, height=350)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     top5 = data.groupby('state')['PM2.5'].mean().nlargest(6).index.tolist()
     df = data[data['state'].isin(top5)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends – Top 6 Most Polluted States', width=600, height=350)
     return chart


</code>
"
194,spatio_temporal_aggregation,Plot the yearly average PM2.5 for the top 10 most polluted states from 2017 to 2024 as a multi-line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    top5 = data.groupby('state')['PM2.5'].mean().nlargest(10).index.tolist()
    df = data[data['state'].isin(top5)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends – Top 10 Most Polluted States', width=600, height=350)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     top5 = data.groupby('state')['PM2.5'].mean().nlargest(10).index.tolist()
     df = data[data['state'].isin(top5)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends – Top 10 Most Polluted States', width=600, height=350)
     return chart


</code>
"
195,specific_pattern,Plot the rolling 30-day average PM2.5 for Meghalaya in 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Meghalaya') & (data['Timestamp'].dt.year == 2024)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Meghalaya 2024', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Meghalaya') & (data['Timestamp'].dt.year == 2024)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Meghalaya 2024', width=600, height=300)


</code>
"
196,temporal_aggregation,Plot the monthly average PM2.5 trend for Uttar Pradesh from 2017 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Uttar Pradesh'].copy()
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='steelblue').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly Average PM2.5 Trend – Uttar Pradesh (2017–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Uttar Pradesh'].copy()
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='steelblue').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly Average PM2.5 Trend – Uttar Pradesh (2017–2024)', width=600, height=300)
     return chart


</code>
"
197,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Brajrajnagar, Aurangabad, and Kaithal in 2018 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Brajrajnagar', 'Aurangabad', 'Kaithal']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2018)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Brajrajnagar vs Aurangabad vs Kaithal – 2018', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Brajrajnagar', 'Aurangabad', 'Kaithal']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2018)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Brajrajnagar vs Aurangabad vs Kaithal – 2018', width=550, height=320)
     return chart


</code>
"
198,spatio_temporal_aggregation,Create a heatmap showing the average PM2.5 by month (x-axis) and year (y-axis) for Kerala.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Kerala'].copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_rect().encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Year:N', title='Year'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='Avg PM2.5'),
        tooltip=['Year:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Heatmap – Kerala (Month × Year)', width=500, height=280)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Kerala'].copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_rect().encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Year:N', title='Year'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='Avg PM2.5'),
         tooltip=['Year:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Heatmap – Kerala (Month × Year)', width=500, height=280)
     return chart


</code>
"
199,population_based,Bar chart of PM2.5 per capita (average PM2.5 × 1000 / population) for each state in 2021.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    pm = data[data['Timestamp'].dt.year == 2021].groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = pm.merge(states_data[['state','population']], on='state')
    df['PM2.5 per Capita (×1000)'] = df['PM2.5'] / df['population'] * 1e6
    df = df.sort_values('PM2.5 per Capita (×1000)', ascending=False)
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5 per Capita (×1000):Q', title='PM2.5 per Million Population'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM2\.5 per Capita (×1000):Q', scale=alt.Scale(scheme='purples'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5 per Capita (×1000):Q', format='.4f')]
    ).properties(title='PM2.5 Per-Capita Pollution Index by State – 2021', width=500, height=400)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     pm = data[data['Timestamp'].dt.year == 2021].groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = pm.merge(states_data[['state','population']], on='state')
     df['PM2.5 per Capita (×1000)'] = df['PM2.5'] / df['population'] * 1e6
     df = df.sort_values('PM2.5 per Capita (×1000)', ascending=False)
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5 per Capita (×1000):Q', title='PM2.5 per Million Population'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM2\.5 per Capita (×1000):Q', scale=alt.Scale(scheme='purples'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5 per Capita (×1000):Q', format='.4f')]
     ).properties(title='PM2.5 Per-Capita Pollution Index by State – 2021', width=500, height=400)
     return chart


</code>
"
200,temporal_aggregation,Plot the monthly average PM2.5 trend for Karnataka from 2017 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Karnataka'].copy()
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='steelblue').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly Average PM2.5 Trend – Karnataka (2017–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Karnataka'].copy()
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='steelblue').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly Average PM2.5 Trend – Karnataka (2017–2024)', width=600, height=300)
     return chart


</code>
"
201,temporal_aggregation,Show the monthly average PM2.5 for Pune in 2019 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Pune') & (data['Timestamp'].dt.year == 2019)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Pune 2019', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Pune') & (data['Timestamp'].dt.year == 2019)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Pune 2019', width=450, height=280)


</code>
"
202,spatial_aggregation,Show a bar chart of the top 10 cities by median PM2.5 in 2023.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2023]
    df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
    df = df.nlargest(10, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
        y=alt.Y('city:N', sort='-x', title='City'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
        tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 10 Cities by Median PM2.5 in 2023', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2023]
     df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
     df = df.nlargest(10, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
         y=alt.Y('city:N', sort='-x', title='City'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
         tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 10 Cities by Median PM2.5 in 2023', width=500, height=300)
     return chart


</code>
"
203,spatial_aggregation,Show a box plot of PM2.5 distribution for each state in 2019.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2019].dropna(subset=['PM2.5'])
    df = df[['state','PM2.5']]
    state_order = df.groupby('state')['PM2.5'].median().sort_values(ascending=False).index.tolist()
    chart = alt.Chart(df).mark_boxplot(extent='min-max').encode(
        x=alt.X('PM2\.5:Q', title='PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort=state_order, title='State'),
        color=alt.Color('state:N', legend=None)
    ).properties(title='PM2.5 Distribution by State – 2019', width=500, height=450)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2019].dropna(subset=['PM2.5'])
     df = df[['state','PM2.5']]
     state_order = df.groupby('state')['PM2.5'].median().sort_values(ascending=False).index.tolist()
     chart = alt.Chart(df).mark_boxplot(extent='min-max').encode(
         x=alt.X('PM2\.5:Q', title='PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort=state_order, title='State'),
         color=alt.Color('state:N', legend=None)
     ).properties(title='PM2.5 Distribution by State – 2019', width=500, height=450)
     return chart


</code>
"
204,specific_pattern,Show PM2.5 exceedances above the Indian standard (60 µg/m³) per year as a bar chart for Tripura.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Tripura'].dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 60].copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby('Year')['Timestamp'].nunique().reset_index()
    df.columns = ['Year','Days Exceeded']
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('Days Exceeded:Q', title='Days PM2.5 > 60 µg/m³'),
        tooltip=['Year:N','Days Exceeded:Q']
    ).properties(title='Days Tripura Exceeded Indian PM2.5 Standard (60 µg/m³) per Year', width=450, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Tripura'].dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 60].copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby('Year')['Timestamp'].nunique().reset_index()
     df.columns = ['Year','Days Exceeded']
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('Days Exceeded:Q', title='Days PM2.5 > 60 µg/m³'),
         tooltip=['Year:N','Days Exceeded:Q']
     ).properties(title='Days Tripura Exceeded Indian PM2.5 Standard (60 µg/m³) per Year', width=450, height=300)
     return chart


</code>
"
205,temporal_aggregation,Plot the monthly average PM2.5 trend for Rajasthan from 2017 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Rajasthan'].copy()
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='steelblue').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly Average PM2.5 Trend – Rajasthan (2017–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Rajasthan'].copy()
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='steelblue').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly Average PM2.5 Trend – Rajasthan (2017–2024)', width=600, height=300)
     return chart


</code>
"
206,spatio_temporal_aggregation,"Create a faceted bar chart showing top 15 states by average PM2.5 per year for 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year.isin([2020,2021,2022,2023])].copy()
    df['Year'] = df['Timestamp'].dt.year
    agg = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    top = agg.groupby('Year').apply(lambda x: x.nlargest(15,'PM2.5')).reset_index(drop=True)
    chart = alt.Chart(top).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Avg PM2.5'),
        y=alt.Y('state:N', sort='-x'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).facet(facet='Year:O', columns=2).properties(title='Top 15 States by PM2.5 per Year')
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year.isin([2020,2021,2022,2023])].copy()
     df['Year'] = df['Timestamp'].dt.year
     agg = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     top = agg.groupby('Year').apply(lambda x: x.nlargest(15,'PM2.5')).reset_index(drop=True)
     chart = alt.Chart(top).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Avg PM2.5'),
         y=alt.Y('state:N', sort='-x'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).facet(facet='Year:O', columns=2).properties(title='Top 15 States by PM2.5 per Year')
     return chart


</code>
"
207,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Karnataka stations in 2019, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Karnataka') & (data['Timestamp'].dt.year == 2019)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Karnataka Stations 2019', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Karnataka') & (data['Timestamp'].dt.year == 2019)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Karnataka Stations 2019', width=450, height=350)


</code>
"
208,specific_pattern,Show a cumulative area chart of PM2.5 readings for Vijayawada across 2019.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Vijayawada') & (data['Timestamp'].dt.year == 2019)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Vijayawada 2019', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Vijayawada') & (data['Timestamp'].dt.year == 2019)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Vijayawada 2019', width=600, height=300)
     return chart


</code>
"
209,temporal_aggregation,Show the monthly average PM10 trend for Pithampur from 2017 to 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Pithampur'].copy()
    df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Pithampur (2017–2022)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Pithampur'].copy()
     df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Pithampur (2017–2022)', width=600, height=300)
     return chart


</code>
"
210,specific_pattern,Show PM2.5 exceedances above the Indian standard (60 µg/m³) per year as a bar chart for West Bengal.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'West Bengal'].dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 60].copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby('Year')['Timestamp'].nunique().reset_index()
    df.columns = ['Year','Days Exceeded']
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('Days Exceeded:Q', title='Days PM2.5 > 60 µg/m³'),
        tooltip=['Year:N','Days Exceeded:Q']
    ).properties(title='Days West Bengal Exceeded Indian PM2.5 Standard (60 µg/m³) per Year', width=450, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'West Bengal'].dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 60].copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby('Year')['Timestamp'].nunique().reset_index()
     df.columns = ['Year','Days Exceeded']
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('Days Exceeded:Q', title='Days PM2.5 > 60 µg/m³'),
         tooltip=['Year:N','Days Exceeded:Q']
     ).properties(title='Days West Bengal Exceeded Indian PM2.5 Standard (60 µg/m³) per Year', width=450, height=300)
     return chart


</code>
"
211,spatial_aggregation,"Create a scatter plot of PM2.5 vs PM10 for all stations in 2023, colored by state.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2023]
    df = df.groupby(['station','state'])[['PM2.5','PM10']].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_point(opacity=0.6, size=60).encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['station:N','state:N',
                 alt.Tooltip('PM2\.5:Q', format='.1f'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='PM2.5 vs PM10 by Station – 2023', width=500, height=400)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2023]
     df = df.groupby(['station','state'])[['PM2.5','PM10']].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_point(opacity=0.6, size=60).encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['station:N','state:N',
                  alt.Tooltip('PM2\.5:Q', format='.1f'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='PM2.5 vs PM10 by Station – 2023', width=500, height=400)
     return chart


</code>
"
212,spatio_temporal_aggregation,"Create a faceted bar chart showing top 13 states by average PM2.5 per year for 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year.isin([2021,2022,2023,2024])].copy()
    df['Year'] = df['Timestamp'].dt.year
    agg = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    top = agg.groupby('Year').apply(lambda x: x.nlargest(13,'PM2.5')).reset_index(drop=True)
    chart = alt.Chart(top).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Avg PM2.5'),
        y=alt.Y('state:N', sort='-x'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).facet(facet='Year:O', columns=2).properties(title='Top 13 States by PM2.5 per Year')
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year.isin([2021,2022,2023,2024])].copy()
     df['Year'] = df['Timestamp'].dt.year
     agg = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     top = agg.groupby('Year').apply(lambda x: x.nlargest(13,'PM2.5')).reset_index(drop=True)
     chart = alt.Chart(top).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Avg PM2.5'),
         y=alt.Y('state:N', sort='-x'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).facet(facet='Year:O', columns=2).properties(title='Top 13 States by PM2.5 per Year')
     return chart


</code>
"
213,spatial_aggregation,Plot the top 9 states by average PM2.5 in 2019 as a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2019]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nlargest(9, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 9 States by Average PM2.5 in 2019', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2019]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nlargest(9, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 9 States by Average PM2.5 in 2019', width=500, height=300)
     return chart


</code>
"
214,spatio_temporal_aggregation,Create a grouped bar chart comparing the average PM2.5 in Winter vs Summer for the top 12 most polluted states.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    top6 = data.groupby('state')['PM2.5'].mean().nlargest(12).index.tolist()
    df = data[data['state'].isin(top6)].copy()
    df['Season'] = df['Timestamp'].dt.month.apply(
        lambda m: 'Winter' if m in [12,1,2] else ('Summer' if m in [3,4,5] else None))
    df = df.dropna(subset=['Season'])
    df = df.groupby(['state','Season'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('state:N', title='State'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('Season:N', title='Season'),
        xOffset='Season:N',
        tooltip=['state:N','Season:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Winter vs Summer PM2.5 – Top 12 Polluted States', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     top6 = data.groupby('state')['PM2.5'].mean().nlargest(12).index.tolist()
     df = data[data['state'].isin(top6)].copy()
     df['Season'] = df['Timestamp'].dt.month.apply(
         lambda m: 'Winter' if m in [12,1,2] else ('Summer' if m in [3,4,5] else None))
     df = df.dropna(subset=['Season'])
     df = df.groupby(['state','Season'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('state:N', title='State'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('Season:N', title='Season'),
         xOffset='Season:N',
         tooltip=['state:N','Season:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Winter vs Summer PM2.5 – Top 12 Polluted States', width=550, height=320)
     return chart


</code>
"
215,temporal_aggregation,Show a monthly bar chart of the number of days Assam exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2023.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Assam') & (data['Timestamp'].dt.year == 2023)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Assam Exceeded WHO PM2.5 Guideline per Month – 2023', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Assam') & (data['Timestamp'].dt.year == 2023)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Assam Exceeded WHO PM2.5 Guideline per Month – 2023', width=500, height=300)
     return chart


</code>
"
216,spatio_temporal_aggregation,Show the monthly average PM2.5 for Manipur across each year from 2017 to 2024 as small multiples (faceted by year).,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Manipur'].copy()
    df['Year'] = df['Timestamp'].dt.year
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='steelblue').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5'),
        tooltip=['Year:O','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).facet(
        facet=alt.Facet('Year:O', title='Year'),
        columns=4
    ).properties(title='Monthly PM2.5 in Manipur by Year (2017–2024)')
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Manipur'].copy()
     df['Year'] = df['Timestamp'].dt.year
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='steelblue').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5'),
         tooltip=['Year:O','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).facet(
         facet=alt.Facet('Year:O', title='Year'),
         columns=4
     ).properties(title='Monthly PM2.5 in Manipur by Year (2017–2024)')
     return chart


</code>
"
217,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Gujarat, Himachal Pradesh, and Tamil Nadu in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Gujarat', 'Himachal Pradesh', 'Tamil Nadu']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Gujarat, Himachal Pradesh, UP – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Gujarat', 'Himachal Pradesh', 'Tamil Nadu']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Gujarat, Himachal Pradesh, UP – 2023', width=550, height=320)
     return chart


</code>
"
218,temporal_aggregation,Show the monthly average PM2.5 for Anantapur in 2018 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Anantapur') & (data['Timestamp'].dt.year == 2018)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Anantapur 2018', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Anantapur') & (data['Timestamp'].dt.year == 2018)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Anantapur 2018', width=450, height=280)


</code>
"
219,spatial_aggregation,Visualize the bottom 12 states with the lowest average PM2.5 in 2018 using a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2018]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nsmallest(12, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Bottom 12 States by Average PM2.5 in 2018', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2018]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nsmallest(12, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Bottom 12 States by Average PM2.5 in 2018', width=500, height=300)
     return chart


</code>
"
220,spatial_aggregation,Plot the distribution of PM2.5 values in Mizoram across all years using a histogram.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Mizoram'].dropna(subset=['PM2.5'])
    df = df[['PM2.5']]
    chart = alt.Chart(df).mark_bar(color='steelblue', opacity=0.75).encode(
        x=alt.X('PM2\.5:Q', bin=alt.Bin(maxbins=40), title='PM2.5 (µg/m³)'),
        y=alt.Y('count()', title='Number of Observations'),
        tooltip=[alt.Tooltip('PM2\.5:Q', bin=True), 'count()']
    ).properties(title='PM2.5 Distribution – Mizoram (All Years)', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Mizoram'].dropna(subset=['PM2.5'])
     df = df[['PM2.5']]
     chart = alt.Chart(df).mark_bar(color='steelblue', opacity=0.75).encode(
         x=alt.X('PM2\.5:Q', bin=alt.Bin(maxbins=40), title='PM2.5 (µg/m³)'),
         y=alt.Y('count()', title='Number of Observations'),
         tooltip=[alt.Tooltip('PM2\.5:Q', bin=True), 'count()']
     ).properties(title='PM2.5 Distribution – Mizoram (All Years)', width=500, height=300)
     return chart


</code>
"
221,temporal_aggregation,Show the monthly average PM2.5 for Nandesari in 2023 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Nandesari') & (data['Timestamp'].dt.year == 2023)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Nandesari 2023', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Nandesari') & (data['Timestamp'].dt.year == 2023)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Nandesari 2023', width=450, height=280)


</code>
"
222,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Punjab, Odisha, and Andhra Pradesh in 2018 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Punjab', 'Odisha', 'Andhra Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2018)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Punjab, Odisha, UP – 2018', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Punjab', 'Odisha', 'Andhra Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2018)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Punjab, Odisha, UP – 2018', width=550, height=320)
     return chart


</code>
"
223,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Chhattisgarh, West Bengal, and Tamil Nadu from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Chhattisgarh', 'West Bengal', 'Tamil Nadu']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Chhattisgarh vs West Bengal vs Tamil Nadu', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Chhattisgarh', 'West Bengal', 'Tamil Nadu']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Chhattisgarh vs West Bengal vs Tamil Nadu', width=550, height=320)
     return chart


</code>
"
224,specific_pattern,Show a cumulative area chart of PM2.5 readings for Saharsa across 2022.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Saharsa') & (data['Timestamp'].dt.year == 2022)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Saharsa 2022', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Saharsa') & (data['Timestamp'].dt.year == 2022)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Saharsa 2022', width=600, height=300)
     return chart


</code>
"
225,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Madhya Pradesh, Bihar, and Chandigarh in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Madhya Pradesh', 'Bihar', 'Chandigarh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Madhya Pradesh, Bihar, UP – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Madhya Pradesh', 'Bihar', 'Chandigarh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Madhya Pradesh, Bihar, UP – 2024', width=550, height=320)
     return chart


</code>
"
226,spatio_temporal_aggregation,Create a grouped bar chart comparing the average PM2.5 in Winter vs Summer for the top 15 most polluted states.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    top6 = data.groupby('state')['PM2.5'].mean().nlargest(15).index.tolist()
    df = data[data['state'].isin(top6)].copy()
    df['Season'] = df['Timestamp'].dt.month.apply(
        lambda m: 'Winter' if m in [12,1,2] else ('Summer' if m in [3,4,5] else None))
    df = df.dropna(subset=['Season'])
    df = df.groupby(['state','Season'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('state:N', title='State'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('Season:N', title='Season'),
        xOffset='Season:N',
        tooltip=['state:N','Season:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Winter vs Summer PM2.5 – Top 15 Polluted States', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     top6 = data.groupby('state')['PM2.5'].mean().nlargest(15).index.tolist()
     df = data[data['state'].isin(top6)].copy()
     df['Season'] = df['Timestamp'].dt.month.apply(
         lambda m: 'Winter' if m in [12,1,2] else ('Summer' if m in [3,4,5] else None))
     df = df.dropna(subset=['Season'])
     df = df.groupby(['state','Season'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('state:N', title='State'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('Season:N', title='Season'),
         xOffset='Season:N',
         tooltip=['state:N','Season:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Winter vs Summer PM2.5 – Top 15 Polluted States', width=550, height=320)
     return chart


</code>
"
227,funding_based,Show a scatter plot of total NCAP funding vs average PM2.5 (2022) per state.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    pm = data[data['Timestamp'].dt.year == 2022].groupby('state')['PM2.5'].mean().reset_index()
    funding = ncap_funding_data.groupby('state')['Total fund released'].sum().reset_index()
    df = pm.merge(funding, on='state').dropna()
    chart = alt.Chart(df).mark_point(filled=True, size=100).encode(
        x=alt.X('Total fund released:Q', title='Total NCAP Funding (Cr)'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 – 2022 (µg/m³)'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f'),
                 alt.Tooltip('Total fund released:Q', format='.1f')]
    ).properties(title='NCAP Funding vs Average PM2.5 by State (2022)', width=450, height=350)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     pm = data[data['Timestamp'].dt.year == 2022].groupby('state')['PM2.5'].mean().reset_index()
     funding = ncap_funding_data.groupby('state')['Total fund released'].sum().reset_index()
     df = pm.merge(funding, on='state').dropna()
     chart = alt.Chart(df).mark_point(filled=True, size=100).encode(
         x=alt.X('Total fund released:Q', title='Total NCAP Funding (Cr)'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 – 2022 (µg/m³)'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f'),
                  alt.Tooltip('Total fund released:Q', format='.1f')]
     ).properties(title='NCAP Funding vs Average PM2.5 by State (2022)', width=450, height=350)
     return chart


</code>
"
228,population_based,"Plot average PM2.5 (2020) vs state population as a scatter plot, labeling each point with the state name.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    pm = data[data['Timestamp'].dt.year == 2020].groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = pm.merge(states_data, on='state')
    points = alt.Chart(df).mark_point(filled=True, size=80).encode(
        x=alt.X('population:Q', title='Population', axis=alt.Axis(format='~s')),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('population:Q', format=',')]
    )
    labels = alt.Chart(df).mark_text(align='left', dx=5, fontSize=9).encode(
        x='population:Q', y='PM2\.5:Q', text='state:N'
    )
    return (points + labels).properties(title='PM2.5 vs Population by State – 2020', width=500, height=400)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     pm = data[data['Timestamp'].dt.year == 2020].groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = pm.merge(states_data, on='state')
     points = alt.Chart(df).mark_point(filled=True, size=80).encode(
         x=alt.X('population:Q', title='Population', axis=alt.Axis(format='~s')),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('population:Q', format=',')]
     )
     labels = alt.Chart(df).mark_text(align='left', dx=5, fontSize=9).encode(
         x='population:Q', y='PM2\.5:Q', text='state:N'
     )
     return (points + labels).properties(title='PM2.5 vs Population by State – 2020', width=500, height=400)


</code>
"
229,temporal_aggregation,Show a monthly bar chart of the number of days Tripura exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2023.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Tripura') & (data['Timestamp'].dt.year == 2023)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Tripura Exceeded WHO PM2.5 Guideline per Month – 2023', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Tripura') & (data['Timestamp'].dt.year == 2023)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Tripura Exceeded WHO PM2.5 Guideline per Month – 2023', width=500, height=300)
     return chart


</code>
"
230,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Thrissur, Karauli, and Jalna in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Thrissur', 'Karauli', 'Jalna']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Thrissur vs Karauli vs Jalna – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Thrissur', 'Karauli', 'Jalna']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Thrissur vs Karauli vs Jalna – 2024', width=550, height=320)
     return chart


</code>
"
231,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Assam, Madhya Pradesh, and Punjab in 2019 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Assam', 'Madhya Pradesh', 'Punjab']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Assam, Madhya Pradesh, UP – 2019', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Assam', 'Madhya Pradesh', 'Punjab']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Assam, Madhya Pradesh, UP – 2019', width=550, height=320)
     return chart


</code>
"
232,spatio_temporal_aggregation,Show the monthly average PM2.5 for Gujarat across each year from 2017 to 2024 as small multiples (faceted by year).,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Gujarat'].copy()
    df['Year'] = df['Timestamp'].dt.year
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='steelblue').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5'),
        tooltip=['Year:O','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).facet(
        facet=alt.Facet('Year:O', title='Year'),
        columns=4
    ).properties(title='Monthly PM2.5 in Gujarat by Year (2017–2024)')
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Gujarat'].copy()
     df['Year'] = df['Timestamp'].dt.year
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='steelblue').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5'),
         tooltip=['Year:O','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).facet(
         facet=alt.Facet('Year:O', title='Year'),
         columns=4
     ).properties(title='Monthly PM2.5 in Gujarat by Year (2017–2024)')
     return chart


</code>
"
233,spatio_temporal_aggregation,Create a heatmap showing the average PM2.5 by month (x-axis) and year (y-axis) for Uttar Pradesh.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Uttar Pradesh'].copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_rect().encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Year:N', title='Year'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='Avg PM2.5'),
        tooltip=['Year:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Heatmap – Uttar Pradesh (Month × Year)', width=500, height=280)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Uttar Pradesh'].copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_rect().encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Year:N', title='Year'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='Avg PM2.5'),
         tooltip=['Year:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Heatmap – Uttar Pradesh (Month × Year)', width=500, height=280)
     return chart


</code>
"
234,funding_based,Visualize NCAP city-level funding for FY 2021-22 as a horizontal bar chart for the top 7 cities.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = ncap_funding_data[['city','state','Amount released during FY 2021-22']].copy()
    df.columns = ['city','state','Amount (Cr)']
    df = df.nlargest(7, 'Amount (Cr)')
    df['City_State'] = df['city'] + ', ' + df['state']
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Amount (Cr):Q', title='Amount Released (Cr)'),
        y=alt.Y('City_State:N', sort='-x', title='City, State'),
        color=alt.Color('state:N', title='State'),
        tooltip=['city:N','state:N', alt.Tooltip('Amount (Cr):Q', format='.1f')]
    ).properties(title='Top 7 Cities by NCAP Funding – FY 2021-22', width=500, height=400)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = ncap_funding_data[['city','state','Amount released during FY 2021-22']].copy()
     df.columns = ['city','state','Amount (Cr)']
     df = df.nlargest(7, 'Amount (Cr)')
     df['City_State'] = df['city'] + ', ' + df['state']
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Amount (Cr):Q', title='Amount Released (Cr)'),
         y=alt.Y('City_State:N', sort='-x', title='City, State'),
         color=alt.Color('state:N', title='State'),
         tooltip=['city:N','state:N', alt.Tooltip('Amount (Cr):Q', format='.1f')]
     ).properties(title='Top 7 Cities by NCAP Funding – FY 2021-22', width=500, height=400)
     return chart


</code>
"
235,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Karnataka, Chandigarh, and Punjab from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Karnataka', 'Chandigarh', 'Punjab']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Karnataka vs Chandigarh vs Punjab', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Karnataka', 'Chandigarh', 'Punjab']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Karnataka vs Chandigarh vs Punjab', width=550, height=320)
     return chart


</code>
"
236,temporal_aggregation,Plot the weekly average PM2.5 for Patna in 2017 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Patna') & (data['Timestamp'].dt.year == 2017)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Patna 2017', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Patna') & (data['Timestamp'].dt.year == 2017)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Patna 2017', width=600, height=300)
     return chart


</code>
"
237,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Meghalaya stations in 2019, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Meghalaya') & (data['Timestamp'].dt.year == 2019)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Meghalaya Stations 2019', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Meghalaya') & (data['Timestamp'].dt.year == 2019)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Meghalaya Stations 2019', width=450, height=350)


</code>
"
238,spatio_temporal_aggregation,Create a grouped bar chart comparing the average PM2.5 in Winter vs Summer for the top 11 most polluted states.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    top6 = data.groupby('state')['PM2.5'].mean().nlargest(11).index.tolist()
    df = data[data['state'].isin(top6)].copy()
    df['Season'] = df['Timestamp'].dt.month.apply(
        lambda m: 'Winter' if m in [12,1,2] else ('Summer' if m in [3,4,5] else None))
    df = df.dropna(subset=['Season'])
    df = df.groupby(['state','Season'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('state:N', title='State'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('Season:N', title='Season'),
        xOffset='Season:N',
        tooltip=['state:N','Season:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Winter vs Summer PM2.5 – Top 11 Polluted States', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     top6 = data.groupby('state')['PM2.5'].mean().nlargest(11).index.tolist()
     df = data[data['state'].isin(top6)].copy()
     df['Season'] = df['Timestamp'].dt.month.apply(
         lambda m: 'Winter' if m in [12,1,2] else ('Summer' if m in [3,4,5] else None))
     df = df.dropna(subset=['Season'])
     df = df.groupby(['state','Season'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('state:N', title='State'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('Season:N', title='Season'),
         xOffset='Season:N',
         tooltip=['state:N','Season:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Winter vs Summer PM2.5 – Top 11 Polluted States', width=550, height=320)
     return chart


</code>
"
239,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Uttar Pradesh stations in 2020, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Uttar Pradesh') & (data['Timestamp'].dt.year == 2020)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Uttar Pradesh Stations 2020', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Uttar Pradesh') & (data['Timestamp'].dt.year == 2020)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Uttar Pradesh Stations 2020', width=450, height=350)


</code>
"
240,area_based,"Create a bubble chart of PM2.5 vs area for each state in 2023, sized by population.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    pm = data[data['Timestamp'].dt.year == 2023].groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = pm.merge(states_data, on='state')
    chart = alt.Chart(df).mark_point(filled=True, opacity=0.7).encode(
        x=alt.X('area (km2):Q', title='Area (km²)', axis=alt.Axis(format='~s')),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        size=alt.Size('population:Q', title='Population', scale=alt.Scale(range=[50,1500])),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f'),
                 alt.Tooltip('population:Q', format=','), alt.Tooltip('area (km2):Q', format=',')]
    ).properties(title='PM2.5 vs Area (size=Population) – 2023', width=500, height=400)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     pm = data[data['Timestamp'].dt.year == 2023].groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = pm.merge(states_data, on='state')
     chart = alt.Chart(df).mark_point(filled=True, opacity=0.7).encode(
         x=alt.X('area (km2):Q', title='Area (km²)', axis=alt.Axis(format='~s')),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         size=alt.Size('population:Q', title='Population', scale=alt.Scale(range=[50,1500])),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f'),
                  alt.Tooltip('population:Q', format=','), alt.Tooltip('area (km2):Q', format=',')]
     ).properties(title='PM2.5 vs Area (size=Population) – 2023', width=500, height=400)
     return chart


</code>
"
241,spatial_aggregation,"Show the top 14 states by average PM10 in 2024 as a bar chart, sorted in descending order.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2024]
    df = df.groupby('state')['PM10'].mean().reset_index().dropna()
    df = df.nlargest(14, 'PM10')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Top 14 States by Average PM10 in 2024', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2024]
     df = df.groupby('state')['PM10'].mean().reset_index().dropna()
     df = df.nlargest(14, 'PM10')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Top 14 States by Average PM10 in 2024', width=500, height=300)
     return chart


</code>
"
242,spatial_aggregation,"Create a scatter plot of PM2.5 vs PM10 for all stations in 2021, colored by state.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2021]
    df = df.groupby(['station','state'])[['PM2.5','PM10']].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_point(opacity=0.6, size=60).encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['station:N','state:N',
                 alt.Tooltip('PM2\.5:Q', format='.1f'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='PM2.5 vs PM10 by Station – 2021', width=500, height=400)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2021]
     df = df.groupby(['station','state'])[['PM2.5','PM10']].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_point(opacity=0.6, size=60).encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['station:N','state:N',
                  alt.Tooltip('PM2\.5:Q', format='.1f'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='PM2.5 vs PM10 by Station – 2021', width=500, height=400)
     return chart


</code>
"
243,spatial_aggregation,"Create a scatter plot of PM2.5 vs PM10 for all stations in 2024, colored by state.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2024]
    df = df.groupby(['station','state'])[['PM2.5','PM10']].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_point(opacity=0.6, size=60).encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['station:N','state:N',
                 alt.Tooltip('PM2\.5:Q', format='.1f'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='PM2.5 vs PM10 by Station – 2024', width=500, height=400)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2024]
     df = df.groupby(['station','state'])[['PM2.5','PM10']].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_point(opacity=0.6, size=60).encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['station:N','state:N',
                  alt.Tooltip('PM2\.5:Q', format='.1f'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='PM2.5 vs PM10 by Station – 2024', width=500, height=400)
     return chart


</code>
"
244,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Haryana, Haryana, and Gujarat from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Haryana', 'Haryana', 'Gujarat']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Haryana vs Haryana vs Gujarat', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Haryana', 'Haryana', 'Gujarat']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Haryana vs Haryana vs Gujarat', width=550, height=320)
     return chart


</code>
"
245,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Andhra Pradesh, Jammu and Kashmir, and Karnataka across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Andhra Pradesh', 'Jammu and Kashmir', 'Karnataka']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Andhra Pradesh', 'Jammu and Kashmir', 'Karnataka']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
246,specific_pattern,Show PM2.5 exceedances above the Indian standard (60 µg/m³) per year as a bar chart for Puducherry.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Puducherry'].dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 60].copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby('Year')['Timestamp'].nunique().reset_index()
    df.columns = ['Year','Days Exceeded']
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('Days Exceeded:Q', title='Days PM2.5 > 60 µg/m³'),
        tooltip=['Year:N','Days Exceeded:Q']
    ).properties(title='Days Puducherry Exceeded Indian PM2.5 Standard (60 µg/m³) per Year', width=450, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Puducherry'].dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 60].copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby('Year')['Timestamp'].nunique().reset_index()
     df.columns = ['Year','Days Exceeded']
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('Days Exceeded:Q', title='Days PM2.5 > 60 µg/m³'),
         tooltip=['Year:N','Days Exceeded:Q']
     ).properties(title='Days Puducherry Exceeded Indian PM2.5 Standard (60 µg/m³) per Year', width=450, height=300)
     return chart


</code>
"
247,temporal_aggregation,Show a monthly bar chart of the number of days Madhya Pradesh exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2024.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Madhya Pradesh') & (data['Timestamp'].dt.year == 2024)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Madhya Pradesh Exceeded WHO PM2.5 Guideline per Month – 2024', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Madhya Pradesh') & (data['Timestamp'].dt.year == 2024)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Madhya Pradesh Exceeded WHO PM2.5 Guideline per Month – 2024', width=500, height=300)
     return chart


</code>
"
248,temporal_aggregation,Show the monthly average PM10 trend for Noida from 2017 to 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Noida'].copy()
    df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Noida (2017–2022)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Noida'].copy()
     df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Noida (2017–2022)', width=600, height=300)
     return chart


</code>
"
249,specific_pattern,Show PM2.5 exceedances above the Indian standard (60 µg/m³) per year as a bar chart for Andhra Pradesh.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Andhra Pradesh'].dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 60].copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby('Year')['Timestamp'].nunique().reset_index()
    df.columns = ['Year','Days Exceeded']
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('Days Exceeded:Q', title='Days PM2.5 > 60 µg/m³'),
        tooltip=['Year:N','Days Exceeded:Q']
    ).properties(title='Days Andhra Pradesh Exceeded Indian PM2.5 Standard (60 µg/m³) per Year', width=450, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Andhra Pradesh'].dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 60].copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby('Year')['Timestamp'].nunique().reset_index()
     df.columns = ['Year','Days Exceeded']
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('Days Exceeded:Q', title='Days PM2.5 > 60 µg/m³'),
         tooltip=['Year:N','Days Exceeded:Q']
     ).properties(title='Days Andhra Pradesh Exceeded Indian PM2.5 Standard (60 µg/m³) per Year', width=450, height=300)
     return chart


</code>
"
250,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Tripura, Chhattisgarh, and Madhya Pradesh across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Tripura', 'Chhattisgarh', 'Madhya Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Tripura', 'Chhattisgarh', 'Madhya Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
251,spatial_aggregation,Plot the average PM2.5 across all states in February 2020 as a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['Timestamp'].dt.year == 2020) & (data['Timestamp'].dt.month == 2)]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('PM2.5', ascending=False)
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='plasma'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Average PM2.5 by State – February 2020', width=500, height=400)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['Timestamp'].dt.year == 2020) & (data['Timestamp'].dt.month == 2)]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('PM2.5', ascending=False)
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='plasma'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Average PM2.5 by State – February 2020', width=500, height=400)
     return chart


</code>
"
252,temporal_aggregation,Plot the weekly average PM2.5 for Ludhiana in 2020 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Ludhiana') & (data['Timestamp'].dt.year == 2020)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Ludhiana 2020', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Ludhiana') & (data['Timestamp'].dt.year == 2020)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Ludhiana 2020', width=600, height=300)
     return chart


</code>
"
253,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Telangana stations in 2019, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Telangana') & (data['Timestamp'].dt.year == 2019)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Telangana Stations 2019', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Telangana') & (data['Timestamp'].dt.year == 2019)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Telangana Stations 2019', width=450, height=350)


</code>
"
254,spatial_aggregation,Plot the median PM10 for each state in summer 2021 (April–June) as a sorted bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['Timestamp'].dt.year == 2021) & (data['Timestamp'].dt.month.isin([4,5,6]))]
    df = df.groupby('state')['PM10'].median().reset_index().dropna()
    df = df.sort_values('PM10', ascending=False)
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM10:Q', title='Median PM10 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM10:Q', scale=alt.Scale(scheme='yelloworangered'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Median PM10 by State – Summer 2021 (Apr–Jun)', width=500, height=400)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['Timestamp'].dt.year == 2021) & (data['Timestamp'].dt.month.isin([4,5,6]))]
     df = df.groupby('state')['PM10'].median().reset_index().dropna()
     df = df.sort_values('PM10', ascending=False)
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM10:Q', title='Median PM10 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM10:Q', scale=alt.Scale(scheme='yelloworangered'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Median PM10 by State – Summer 2021 (Apr–Jun)', width=500, height=400)
     return chart


</code>
"
255,spatial_aggregation,"Show the top 13 states by average PM10 in 2023 as a bar chart, sorted in descending order.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2023]
    df = df.groupby('state')['PM10'].mean().reset_index().dropna()
    df = df.nlargest(13, 'PM10')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Top 13 States by Average PM10 in 2023', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2023]
     df = df.groupby('state')['PM10'].mean().reset_index().dropna()
     df = df.nlargest(13, 'PM10')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Top 13 States by Average PM10 in 2023', width=500, height=300)
     return chart


</code>
"
256,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Odisha, Sikkim, and Assam from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Odisha', 'Sikkim', 'Assam']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Odisha vs Sikkim vs Assam', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Odisha', 'Sikkim', 'Assam']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Odisha vs Sikkim vs Assam', width=550, height=320)
     return chart


</code>
"
257,spatial_aggregation,Visualize the bottom 5 states with the lowest average PM2.5 in 2024 using a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2024]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nsmallest(5, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Bottom 5 States by Average PM2.5 in 2024', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2024]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nsmallest(5, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Bottom 5 States by Average PM2.5 in 2024', width=500, height=300)
     return chart


</code>
"
258,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Andhra Pradesh, Telangana, and Nagaland in 2018 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Andhra Pradesh', 'Telangana', 'Nagaland']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2018)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Andhra Pradesh, Telangana, UP – 2018', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Andhra Pradesh', 'Telangana', 'Nagaland']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2018)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Andhra Pradesh, Telangana, UP – 2018', width=550, height=320)
     return chart


</code>
"
259,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Puducherry, Maharashtra, and Bihar in 2018 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Puducherry', 'Maharashtra', 'Bihar']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2018)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Puducherry, Maharashtra, UP – 2018', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Puducherry', 'Maharashtra', 'Bihar']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2018)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Puducherry, Maharashtra, UP – 2018', width=550, height=320)
     return chart


</code>
"
260,spatio_temporal_aggregation,Show a heatmap of average PM2.5 for the top 14 most polluted states by month for 2018.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    top8 = data.groupby('state')['PM2.5'].mean().nlargest(14).index.tolist()
    df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2018)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_rect().encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('state:N', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
        tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Heatmap – Top 14 Polluted States by Month (2018)', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     top8 = data.groupby('state')['PM2.5'].mean().nlargest(14).index.tolist()
     df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2018)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_rect().encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('state:N', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
         tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Heatmap – Top 14 Polluted States by Month (2018)', width=500, height=300)
     return chart


</code>
"
261,temporal_aggregation,Plot the monthly average PM2.5 trend for Sikkim from 2017 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Sikkim'].copy()
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='steelblue').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly Average PM2.5 Trend – Sikkim (2017–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Sikkim'].copy()
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='steelblue').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly Average PM2.5 Trend – Sikkim (2017–2024)', width=600, height=300)
     return chart


</code>
"
262,temporal_aggregation,Show a monthly bar chart of the number of days Bihar exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2020.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Bihar') & (data['Timestamp'].dt.year == 2020)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Bihar Exceeded WHO PM2.5 Guideline per Month – 2020', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Bihar') & (data['Timestamp'].dt.year == 2020)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Bihar Exceeded WHO PM2.5 Guideline per Month – 2020', width=500, height=300)
     return chart


</code>
"
263,area_based,"Create a bubble chart of PM2.5 vs area for each state in 2017, sized by population.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    pm = data[data['Timestamp'].dt.year == 2017].groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = pm.merge(states_data, on='state')
    chart = alt.Chart(df).mark_point(filled=True, opacity=0.7).encode(
        x=alt.X('area (km2):Q', title='Area (km²)', axis=alt.Axis(format='~s')),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        size=alt.Size('population:Q', title='Population', scale=alt.Scale(range=[50,1500])),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f'),
                 alt.Tooltip('population:Q', format=','), alt.Tooltip('area (km2):Q', format=',')]
    ).properties(title='PM2.5 vs Area (size=Population) – 2017', width=500, height=400)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     pm = data[data['Timestamp'].dt.year == 2017].groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = pm.merge(states_data, on='state')
     chart = alt.Chart(df).mark_point(filled=True, opacity=0.7).encode(
         x=alt.X('area (km2):Q', title='Area (km²)', axis=alt.Axis(format='~s')),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         size=alt.Size('population:Q', title='Population', scale=alt.Scale(range=[50,1500])),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f'),
                  alt.Tooltip('population:Q', format=','), alt.Tooltip('area (km2):Q', format=',')]
     ).properties(title='PM2.5 vs Area (size=Population) – 2017', width=500, height=400)
     return chart


</code>
"
264,spatio_temporal_aggregation,"Create a faceted bar chart showing top 11 states by average PM2.5 per year for 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year.isin([2021,2022,2023,2024])].copy()
    df['Year'] = df['Timestamp'].dt.year
    agg = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    top = agg.groupby('Year').apply(lambda x: x.nlargest(11,'PM2.5')).reset_index(drop=True)
    chart = alt.Chart(top).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Avg PM2.5'),
        y=alt.Y('state:N', sort='-x'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).facet(facet='Year:O', columns=2).properties(title='Top 11 States by PM2.5 per Year')
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year.isin([2021,2022,2023,2024])].copy()
     df['Year'] = df['Timestamp'].dt.year
     agg = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     top = agg.groupby('Year').apply(lambda x: x.nlargest(11,'PM2.5')).reset_index(drop=True)
     chart = alt.Chart(top).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Avg PM2.5'),
         y=alt.Y('state:N', sort='-x'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).facet(facet='Year:O', columns=2).properties(title='Top 11 States by PM2.5 per Year')
     return chart


</code>
"
265,temporal_aggregation,Show the monthly average PM2.5 for Jalandhar in 2017 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Jalandhar') & (data['Timestamp'].dt.year == 2017)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Jalandhar 2017', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Jalandhar') & (data['Timestamp'].dt.year == 2017)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Jalandhar 2017', width=450, height=280)


</code>
"
266,spatio_temporal_aggregation,"Create a faceted bar chart showing top 14 states by average PM2.5 per year for 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year.isin([2017,2018,2019,2020])].copy()
    df['Year'] = df['Timestamp'].dt.year
    agg = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    top = agg.groupby('Year').apply(lambda x: x.nlargest(14,'PM2.5')).reset_index(drop=True)
    chart = alt.Chart(top).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Avg PM2.5'),
        y=alt.Y('state:N', sort='-x'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).facet(facet='Year:O', columns=2).properties(title='Top 14 States by PM2.5 per Year')
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year.isin([2017,2018,2019,2020])].copy()
     df['Year'] = df['Timestamp'].dt.year
     agg = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     top = agg.groupby('Year').apply(lambda x: x.nlargest(14,'PM2.5')).reset_index(drop=True)
     chart = alt.Chart(top).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Avg PM2.5'),
         y=alt.Y('state:N', sort='-x'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).facet(facet='Year:O', columns=2).properties(title='Top 14 States by PM2.5 per Year')
     return chart


</code>
"
267,spatial_aggregation,Visualize the bottom 13 states with the lowest average PM2.5 in 2019 using a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2019]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nsmallest(13, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Bottom 13 States by Average PM2.5 in 2019', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2019]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nsmallest(13, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Bottom 13 States by Average PM2.5 in 2019', width=500, height=300)
     return chart


</code>
"
268,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Assam, Odisha, and Manipur across 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Assam', 'Odisha', 'Manipur']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Assam', 'Odisha', 'Manipur']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
     return chart


</code>
"
269,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Maharashtra stations in 2024, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Maharashtra') & (data['Timestamp'].dt.year == 2024)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Maharashtra Stations 2024', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Maharashtra') & (data['Timestamp'].dt.year == 2024)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Maharashtra Stations 2024', width=450, height=350)


</code>
"
270,spatio_temporal_aggregation,Show a heatmap of average PM2.5 for the top 12 most polluted states by month for 2023.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    top8 = data.groupby('state')['PM2.5'].mean().nlargest(12).index.tolist()
    df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_rect().encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('state:N', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
        tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Heatmap – Top 12 Polluted States by Month (2023)', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     top8 = data.groupby('state')['PM2.5'].mean().nlargest(12).index.tolist()
     df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_rect().encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('state:N', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
         tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Heatmap – Top 12 Polluted States by Month (2023)', width=500, height=300)
     return chart


</code>
"
271,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Rajasthan, West Bengal, and Manipur across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Rajasthan', 'West Bengal', 'Manipur']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Rajasthan', 'West Bengal', 'Manipur']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
272,spatio_temporal_aggregation,Plot the yearly average PM2.5 for the top 14 most polluted states from 2017 to 2024 as a multi-line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    top5 = data.groupby('state')['PM2.5'].mean().nlargest(14).index.tolist()
    df = data[data['state'].isin(top5)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends – Top 14 Most Polluted States', width=600, height=350)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     top5 = data.groupby('state')['PM2.5'].mean().nlargest(14).index.tolist()
     df = data[data['state'].isin(top5)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends – Top 14 Most Polluted States', width=600, height=350)
     return chart


</code>
"
273,funding_based,Visualize NCAP city-level funding for FY 2021-22 as a horizontal bar chart for the top 12 cities.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = ncap_funding_data[['city','state','Amount released during FY 2021-22']].copy()
    df.columns = ['city','state','Amount (Cr)']
    df = df.nlargest(12, 'Amount (Cr)')
    df['City_State'] = df['city'] + ', ' + df['state']
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Amount (Cr):Q', title='Amount Released (Cr)'),
        y=alt.Y('City_State:N', sort='-x', title='City, State'),
        color=alt.Color('state:N', title='State'),
        tooltip=['city:N','state:N', alt.Tooltip('Amount (Cr):Q', format='.1f')]
    ).properties(title='Top 12 Cities by NCAP Funding – FY 2021-22', width=500, height=400)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = ncap_funding_data[['city','state','Amount released during FY 2021-22']].copy()
     df.columns = ['city','state','Amount (Cr)']
     df = df.nlargest(12, 'Amount (Cr)')
     df['City_State'] = df['city'] + ', ' + df['state']
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Amount (Cr):Q', title='Amount Released (Cr)'),
         y=alt.Y('City_State:N', sort='-x', title='City, State'),
         color=alt.Color('state:N', title='State'),
         tooltip=['city:N','state:N', alt.Tooltip('Amount (Cr):Q', format='.1f')]
     ).properties(title='Top 12 Cities by NCAP Funding – FY 2021-22', width=500, height=400)
     return chart


</code>
"
274,area_based,"Create a bubble chart of PM2.5 vs area for each state in 2019, sized by population.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    pm = data[data['Timestamp'].dt.year == 2019].groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = pm.merge(states_data, on='state')
    chart = alt.Chart(df).mark_point(filled=True, opacity=0.7).encode(
        x=alt.X('area (km2):Q', title='Area (km²)', axis=alt.Axis(format='~s')),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        size=alt.Size('population:Q', title='Population', scale=alt.Scale(range=[50,1500])),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f'),
                 alt.Tooltip('population:Q', format=','), alt.Tooltip('area (km2):Q', format=',')]
    ).properties(title='PM2.5 vs Area (size=Population) – 2019', width=500, height=400)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     pm = data[data['Timestamp'].dt.year == 2019].groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = pm.merge(states_data, on='state')
     chart = alt.Chart(df).mark_point(filled=True, opacity=0.7).encode(
         x=alt.X('area (km2):Q', title='Area (km²)', axis=alt.Axis(format='~s')),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         size=alt.Size('population:Q', title='Population', scale=alt.Scale(range=[50,1500])),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f'),
                  alt.Tooltip('population:Q', format=','), alt.Tooltip('area (km2):Q', format=',')]
     ).properties(title='PM2.5 vs Area (size=Population) – 2019', width=500, height=400)
     return chart


</code>
"
275,spatio_temporal_aggregation,Create a heatmap showing the average PM2.5 by month (x-axis) and year (y-axis) for Tripura.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Tripura'].copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_rect().encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Year:N', title='Year'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='Avg PM2.5'),
        tooltip=['Year:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Heatmap – Tripura (Month × Year)', width=500, height=280)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Tripura'].copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_rect().encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Year:N', title='Year'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='Avg PM2.5'),
         tooltip=['Year:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Heatmap – Tripura (Month × Year)', width=500, height=280)
     return chart


</code>
"
276,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Assam, Uttarakhand, and Haryana in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Assam', 'Uttarakhand', 'Haryana']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Assam, Uttarakhand, UP – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Assam', 'Uttarakhand', 'Haryana']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Assam, Uttarakhand, UP – 2024', width=550, height=320)
     return chart


</code>
"
277,spatio_temporal_aggregation,Create a grouped bar chart comparing the average PM2.5 in Winter vs Summer for the top 14 most polluted states.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    top6 = data.groupby('state')['PM2.5'].mean().nlargest(14).index.tolist()
    df = data[data['state'].isin(top6)].copy()
    df['Season'] = df['Timestamp'].dt.month.apply(
        lambda m: 'Winter' if m in [12,1,2] else ('Summer' if m in [3,4,5] else None))
    df = df.dropna(subset=['Season'])
    df = df.groupby(['state','Season'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('state:N', title='State'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('Season:N', title='Season'),
        xOffset='Season:N',
        tooltip=['state:N','Season:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Winter vs Summer PM2.5 – Top 14 Polluted States', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     top6 = data.groupby('state')['PM2.5'].mean().nlargest(14).index.tolist()
     df = data[data['state'].isin(top6)].copy()
     df['Season'] = df['Timestamp'].dt.month.apply(
         lambda m: 'Winter' if m in [12,1,2] else ('Summer' if m in [3,4,5] else None))
     df = df.dropna(subset=['Season'])
     df = df.groupby(['state','Season'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('state:N', title='State'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('Season:N', title='Season'),
         xOffset='Season:N',
         tooltip=['state:N','Season:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Winter vs Summer PM2.5 – Top 14 Polluted States', width=550, height=320)
     return chart


</code>
"
278,spatial_aggregation,Show a box plot of PM2.5 distribution for each state in 2023.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2023].dropna(subset=['PM2.5'])
    df = df[['state','PM2.5']]
    state_order = df.groupby('state')['PM2.5'].median().sort_values(ascending=False).index.tolist()
    chart = alt.Chart(df).mark_boxplot(extent='min-max').encode(
        x=alt.X('PM2\.5:Q', title='PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort=state_order, title='State'),
        color=alt.Color('state:N', legend=None)
    ).properties(title='PM2.5 Distribution by State – 2023', width=500, height=450)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2023].dropna(subset=['PM2.5'])
     df = df[['state','PM2.5']]
     state_order = df.groupby('state')['PM2.5'].median().sort_values(ascending=False).index.tolist()
     chart = alt.Chart(df).mark_boxplot(extent='min-max').encode(
         x=alt.X('PM2\.5:Q', title='PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort=state_order, title='State'),
         color=alt.Color('state:N', legend=None)
     ).properties(title='PM2.5 Distribution by State – 2023', width=500, height=450)
     return chart


</code>
"
279,spatial_aggregation,"Show the top 10 states by average PM10 in 2021 as a bar chart, sorted in descending order.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2021]
    df = df.groupby('state')['PM10'].mean().reset_index().dropna()
    df = df.nlargest(10, 'PM10')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Top 10 States by Average PM10 in 2021', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2021]
     df = df.groupby('state')['PM10'].mean().reset_index().dropna()
     df = df.nlargest(10, 'PM10')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Top 10 States by Average PM10 in 2021', width=500, height=300)
     return chart


</code>
"
280,spatial_aggregation,Plot the top 8 states by average PM2.5 in 2022 as a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2022]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nlargest(8, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 8 States by Average PM2.5 in 2022', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2022]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nlargest(8, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 8 States by Average PM2.5 in 2022', width=500, height=300)
     return chart


</code>
"
281,spatial_aggregation,Plot the median PM10 for each state in summer 2018 (April–June) as a sorted bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month.isin([4,5,6]))]
    df = df.groupby('state')['PM10'].median().reset_index().dropna()
    df = df.sort_values('PM10', ascending=False)
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM10:Q', title='Median PM10 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM10:Q', scale=alt.Scale(scheme='yelloworangered'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Median PM10 by State – Summer 2018 (Apr–Jun)', width=500, height=400)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['Timestamp'].dt.year == 2018) & (data['Timestamp'].dt.month.isin([4,5,6]))]
     df = df.groupby('state')['PM10'].median().reset_index().dropna()
     df = df.sort_values('PM10', ascending=False)
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM10:Q', title='Median PM10 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM10:Q', scale=alt.Scale(scheme='yelloworangered'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Median PM10 by State – Summer 2018 (Apr–Jun)', width=500, height=400)
     return chart


</code>
"
282,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Meghalaya stations in 2020, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Meghalaya') & (data['Timestamp'].dt.year == 2020)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Meghalaya Stations 2020', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Meghalaya') & (data['Timestamp'].dt.year == 2020)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Meghalaya Stations 2020', width=450, height=350)


</code>
"
283,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Odisha, Maharashtra, and Punjab in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Odisha', 'Maharashtra', 'Punjab']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Odisha, Maharashtra, UP – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Odisha', 'Maharashtra', 'Punjab']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Odisha, Maharashtra, UP – 2023', width=550, height=320)
     return chart


</code>
"
284,spatial_aggregation,"Show the top 9 states by average PM10 in 2017 as a bar chart, sorted in descending order.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2017]
    df = df.groupby('state')['PM10'].mean().reset_index().dropna()
    df = df.nlargest(9, 'PM10')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Top 9 States by Average PM10 in 2017', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2017]
     df = df.groupby('state')['PM10'].mean().reset_index().dropna()
     df = df.nlargest(9, 'PM10')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Top 9 States by Average PM10 in 2017', width=500, height=300)
     return chart


</code>
"
285,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Karnataka, Gujarat, and Punjab from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Karnataka', 'Gujarat', 'Punjab']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Karnataka vs Gujarat vs Punjab', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Karnataka', 'Gujarat', 'Punjab']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Karnataka vs Gujarat vs Punjab', width=550, height=320)
     return chart


</code>
"
286,spatio_temporal_aggregation,"Create a faceted bar chart showing top 12 states by average PM2.5 per year for 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year.isin([2020,2021,2022,2023])].copy()
    df['Year'] = df['Timestamp'].dt.year
    agg = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    top = agg.groupby('Year').apply(lambda x: x.nlargest(12,'PM2.5')).reset_index(drop=True)
    chart = alt.Chart(top).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Avg PM2.5'),
        y=alt.Y('state:N', sort='-x'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).facet(facet='Year:O', columns=2).properties(title='Top 12 States by PM2.5 per Year')
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year.isin([2020,2021,2022,2023])].copy()
     df['Year'] = df['Timestamp'].dt.year
     agg = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     top = agg.groupby('Year').apply(lambda x: x.nlargest(12,'PM2.5')).reset_index(drop=True)
     chart = alt.Chart(top).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Avg PM2.5'),
         y=alt.Y('state:N', sort='-x'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).facet(facet='Year:O', columns=2).properties(title='Top 12 States by PM2.5 per Year')
     return chart


</code>
"
287,population_based,"Plot average PM2.5 (2022) vs state population as a scatter plot, labeling each point with the state name.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    pm = data[data['Timestamp'].dt.year == 2022].groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = pm.merge(states_data, on='state')
    points = alt.Chart(df).mark_point(filled=True, size=80).encode(
        x=alt.X('population:Q', title='Population', axis=alt.Axis(format='~s')),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('population:Q', format=',')]
    )
    labels = alt.Chart(df).mark_text(align='left', dx=5, fontSize=9).encode(
        x='population:Q', y='PM2\.5:Q', text='state:N'
    )
    return (points + labels).properties(title='PM2.5 vs Population by State – 2022', width=500, height=400)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     pm = data[data['Timestamp'].dt.year == 2022].groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = pm.merge(states_data, on='state')
     points = alt.Chart(df).mark_point(filled=True, size=80).encode(
         x=alt.X('population:Q', title='Population', axis=alt.Axis(format='~s')),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('population:Q', format=',')]
     )
     labels = alt.Chart(df).mark_text(align='left', dx=5, fontSize=9).encode(
         x='population:Q', y='PM2\.5:Q', text='state:N'
     )
     return (points + labels).properties(title='PM2.5 vs Population by State – 2022', width=500, height=400)


</code>
"
288,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Jharkhand, Madhya Pradesh, and Punjab across 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Jharkhand', 'Madhya Pradesh', 'Punjab']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Jharkhand', 'Madhya Pradesh', 'Punjab']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
     return chart


</code>
"
289,temporal_aggregation,Show the monthly average PM2.5 for Silchar in 2018 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Silchar') & (data['Timestamp'].dt.year == 2018)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Silchar 2018', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Silchar') & (data['Timestamp'].dt.year == 2018)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Silchar 2018', width=450, height=280)


</code>
"
290,spatio_temporal_aggregation,Show the monthly average PM2.5 for Bihar across each year from 2017 to 2024 as small multiples (faceted by year).,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Bihar'].copy()
    df['Year'] = df['Timestamp'].dt.year
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='steelblue').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5'),
        tooltip=['Year:O','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).facet(
        facet=alt.Facet('Year:O', title='Year'),
        columns=4
    ).properties(title='Monthly PM2.5 in Bihar by Year (2017–2024)')
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Bihar'].copy()
     df['Year'] = df['Timestamp'].dt.year
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='steelblue').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5'),
         tooltip=['Year:O','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).facet(
         facet=alt.Facet('Year:O', title='Year'),
         columns=4
     ).properties(title='Monthly PM2.5 in Bihar by Year (2017–2024)')
     return chart


</code>
"
291,temporal_aggregation,Show the monthly average PM2.5 for Howrah in 2019 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Howrah') & (data['Timestamp'].dt.year == 2019)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Howrah 2019', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Howrah') & (data['Timestamp'].dt.year == 2019)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Howrah 2019', width=450, height=280)


</code>
"
292,spatio_temporal_aggregation,Show a heatmap of average PM2.5 for the top 9 most polluted states by month for 2018.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    top8 = data.groupby('state')['PM2.5'].mean().nlargest(9).index.tolist()
    df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2018)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_rect().encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('state:N', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
        tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Heatmap – Top 9 Polluted States by Month (2018)', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     top8 = data.groupby('state')['PM2.5'].mean().nlargest(9).index.tolist()
     df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2018)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_rect().encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('state:N', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
         tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Heatmap – Top 9 Polluted States by Month (2018)', width=500, height=300)
     return chart


</code>
"
293,spatial_aggregation,Plot the top 13 states by average PM2.5 in 2023 as a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2023]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nlargest(13, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 13 States by Average PM2.5 in 2023', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2023]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nlargest(13, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 13 States by Average PM2.5 in 2023', width=500, height=300)
     return chart


</code>
"
294,temporal_aggregation,Show the monthly average PM10 trend for Baghpat from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Baghpat'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Baghpat (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Baghpat'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Baghpat (2019–2024)', width=600, height=300)
     return chart


</code>
"
295,spatio_temporal_aggregation,Show the monthly average PM2.5 for Punjab across each year from 2017 to 2024 as small multiples (faceted by year).,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Punjab'].copy()
    df['Year'] = df['Timestamp'].dt.year
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='steelblue').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5'),
        tooltip=['Year:O','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).facet(
        facet=alt.Facet('Year:O', title='Year'),
        columns=4
    ).properties(title='Monthly PM2.5 in Punjab by Year (2017–2024)')
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Punjab'].copy()
     df['Year'] = df['Timestamp'].dt.year
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='steelblue').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5'),
         tooltip=['Year:O','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).facet(
         facet=alt.Facet('Year:O', title='Year'),
         columns=4
     ).properties(title='Monthly PM2.5 in Punjab by Year (2017–2024)')
     return chart


</code>
"
296,temporal_aggregation,Show the monthly average PM10 trend for Bilaspur from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Bilaspur'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Bilaspur (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Bilaspur'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Bilaspur (2019–2024)', width=600, height=300)
     return chart


</code>
"
297,spatial_aggregation,Plot the top 9 states by average PM2.5 in 2022 as a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2022]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nlargest(9, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 9 States by Average PM2.5 in 2022', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2022]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nlargest(9, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 9 States by Average PM2.5 in 2022', width=500, height=300)
     return chart


</code>
"
298,spatial_aggregation,Show a bar chart of the top 12 cities by median PM2.5 in 2020.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2020]
    df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
    df = df.nlargest(12, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
        y=alt.Y('city:N', sort='-x', title='City'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
        tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 12 Cities by Median PM2.5 in 2020', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2020]
     df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
     df = df.nlargest(12, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
         y=alt.Y('city:N', sort='-x', title='City'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
         tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 12 Cities by Median PM2.5 in 2020', width=500, height=300)
     return chart


</code>
"
299,spatial_aggregation,Show a bar chart of the top 15 cities by median PM2.5 in 2021.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2021]
    df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
    df = df.nlargest(15, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
        y=alt.Y('city:N', sort='-x', title='City'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
        tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 15 Cities by Median PM2.5 in 2021', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2021]
     df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
     df = df.nlargest(15, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
         y=alt.Y('city:N', sort='-x', title='City'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
         tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 15 Cities by Median PM2.5 in 2021', width=500, height=300)
     return chart


</code>
"
300,specific_pattern,Plot the rolling 30-day average PM2.5 for Chandigarh in 2017 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Chandigarh') & (data['Timestamp'].dt.year == 2017)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Chandigarh 2017', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Chandigarh') & (data['Timestamp'].dt.year == 2017)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Chandigarh 2017', width=600, height=300)


</code>
"
301,area_based,Show the PM2.5 per 1000 km² (air quality density) for each state in 2019 as a bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    pm = data[data['Timestamp'].dt.year == 2019].groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = pm.merge(states_data[['state','area (km2)']], on='state')
    df['PM2.5 per 1000 km²'] = df['PM2.5'] / df['area (km2)'] * 1000
    df = df.sort_values('PM2.5 per 1000 km²', ascending=False)
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5 per 1000 km²:Q', title='PM2.5 per 1000 km²'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM2\.5 per 1000 km²:Q', scale=alt.Scale(scheme='orangered'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5 per 1000 km²:Q', format='.3f')]
    ).properties(title='Air Quality Density (PM2.5 per 1000 km²) by State – 2019', width=500, height=400)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     pm = data[data['Timestamp'].dt.year == 2019].groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = pm.merge(states_data[['state','area (km2)']], on='state')
     df['PM2.5 per 1000 km²'] = df['PM2.5'] / df['area (km2)'] * 1000
     df = df.sort_values('PM2.5 per 1000 km²', ascending=False)
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5 per 1000 km²:Q', title='PM2.5 per 1000 km²'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM2\.5 per 1000 km²:Q', scale=alt.Scale(scheme='orangered'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5 per 1000 km²:Q', format='.3f')]
     ).properties(title='Air Quality Density (PM2.5 per 1000 km²) by State – 2019', width=500, height=400)
     return chart


</code>
"
302,temporal_aggregation,Plot the monthly average PM2.5 trend for Meghalaya from 2017 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Meghalaya'].copy()
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='steelblue').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly Average PM2.5 Trend – Meghalaya (2017–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Meghalaya'].copy()
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='steelblue').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly Average PM2.5 Trend – Meghalaya (2017–2024)', width=600, height=300)
     return chart


</code>
"
303,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Nagaland stations in 2021, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Nagaland') & (data['Timestamp'].dt.year == 2021)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Nagaland Stations 2021', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Nagaland') & (data['Timestamp'].dt.year == 2021)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Nagaland Stations 2021', width=450, height=350)


</code>
"
304,spatial_aggregation,Show a box plot of PM2.5 distribution for each state in 2018.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2018].dropna(subset=['PM2.5'])
    df = df[['state','PM2.5']]
    state_order = df.groupby('state')['PM2.5'].median().sort_values(ascending=False).index.tolist()
    chart = alt.Chart(df).mark_boxplot(extent='min-max').encode(
        x=alt.X('PM2\.5:Q', title='PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort=state_order, title='State'),
        color=alt.Color('state:N', legend=None)
    ).properties(title='PM2.5 Distribution by State – 2018', width=500, height=450)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2018].dropna(subset=['PM2.5'])
     df = df[['state','PM2.5']]
     state_order = df.groupby('state')['PM2.5'].median().sort_values(ascending=False).index.tolist()
     chart = alt.Chart(df).mark_boxplot(extent='min-max').encode(
         x=alt.X('PM2\.5:Q', title='PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort=state_order, title='State'),
         color=alt.Color('state:N', legend=None)
     ).properties(title='PM2.5 Distribution by State – 2018', width=500, height=450)
     return chart


</code>
"
305,funding_based,Show a scatter plot of total NCAP funding vs average PM2.5 (2021) per state.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    pm = data[data['Timestamp'].dt.year == 2021].groupby('state')['PM2.5'].mean().reset_index()
    funding = ncap_funding_data.groupby('state')['Total fund released'].sum().reset_index()
    df = pm.merge(funding, on='state').dropna()
    chart = alt.Chart(df).mark_point(filled=True, size=100).encode(
        x=alt.X('Total fund released:Q', title='Total NCAP Funding (Cr)'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 – 2021 (µg/m³)'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f'),
                 alt.Tooltip('Total fund released:Q', format='.1f')]
    ).properties(title='NCAP Funding vs Average PM2.5 by State (2021)', width=450, height=350)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     pm = data[data['Timestamp'].dt.year == 2021].groupby('state')['PM2.5'].mean().reset_index()
     funding = ncap_funding_data.groupby('state')['Total fund released'].sum().reset_index()
     df = pm.merge(funding, on='state').dropna()
     chart = alt.Chart(df).mark_point(filled=True, size=100).encode(
         x=alt.X('Total fund released:Q', title='Total NCAP Funding (Cr)'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 – 2021 (µg/m³)'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f'),
                  alt.Tooltip('Total fund released:Q', format='.1f')]
     ).properties(title='NCAP Funding vs Average PM2.5 by State (2021)', width=450, height=350)
     return chart


</code>
"
306,spatio_temporal_aggregation,"Create a faceted bar chart showing top 9 states by average PM2.5 per year for 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year.isin([2021,2022,2023,2024])].copy()
    df['Year'] = df['Timestamp'].dt.year
    agg = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    top = agg.groupby('Year').apply(lambda x: x.nlargest(9,'PM2.5')).reset_index(drop=True)
    chart = alt.Chart(top).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Avg PM2.5'),
        y=alt.Y('state:N', sort='-x'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).facet(facet='Year:O', columns=2).properties(title='Top 9 States by PM2.5 per Year')
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year.isin([2021,2022,2023,2024])].copy()
     df['Year'] = df['Timestamp'].dt.year
     agg = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     top = agg.groupby('Year').apply(lambda x: x.nlargest(9,'PM2.5')).reset_index(drop=True)
     chart = alt.Chart(top).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Avg PM2.5'),
         y=alt.Y('state:N', sort='-x'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).facet(facet='Year:O', columns=2).properties(title='Top 9 States by PM2.5 per Year')
     return chart


</code>
"
307,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Jaipur, Davanagere, and Bharatpur in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Jaipur', 'Davanagere', 'Bharatpur']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Jaipur vs Davanagere vs Bharatpur – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Jaipur', 'Davanagere', 'Bharatpur']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Jaipur vs Davanagere vs Bharatpur – 2023', width=550, height=320)
     return chart


</code>
"
308,temporal_aggregation,Show a monthly bar chart of the number of days Kerala exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2017.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Kerala') & (data['Timestamp'].dt.year == 2017)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Kerala Exceeded WHO PM2.5 Guideline per Month – 2017', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Kerala') & (data['Timestamp'].dt.year == 2017)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Kerala Exceeded WHO PM2.5 Guideline per Month – 2017', width=500, height=300)
     return chart


</code>
"
309,spatio_temporal_aggregation,Show the monthly average PM2.5 for Arunachal Pradesh across each year from 2017 to 2024 as small multiples (faceted by year).,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Arunachal Pradesh'].copy()
    df['Year'] = df['Timestamp'].dt.year
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='steelblue').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5'),
        tooltip=['Year:O','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).facet(
        facet=alt.Facet('Year:O', title='Year'),
        columns=4
    ).properties(title='Monthly PM2.5 in Arunachal Pradesh by Year (2017–2024)')
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Arunachal Pradesh'].copy()
     df['Year'] = df['Timestamp'].dt.year
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='steelblue').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5'),
         tooltip=['Year:O','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).facet(
         facet=alt.Facet('Year:O', title='Year'),
         columns=4
     ).properties(title='Monthly PM2.5 in Arunachal Pradesh by Year (2017–2024)')
     return chart


</code>
"
310,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Himachal Pradesh, Tripura, and Punjab from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Himachal Pradesh', 'Tripura', 'Punjab']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Himachal Pradesh vs Tripura vs Punjab', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Himachal Pradesh', 'Tripura', 'Punjab']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Himachal Pradesh vs Tripura vs Punjab', width=550, height=320)
     return chart


</code>
"
311,specific_pattern,Plot the rolling 30-day average PM2.5 for Uttarakhand in 2018 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Uttarakhand') & (data['Timestamp'].dt.year == 2018)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Uttarakhand 2018', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Uttarakhand') & (data['Timestamp'].dt.year == 2018)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Uttarakhand 2018', width=600, height=300)


</code>
"
312,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Buxar, Prayagraj, and Ghaziabad in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Buxar', 'Prayagraj', 'Ghaziabad']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Buxar vs Prayagraj vs Ghaziabad – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Buxar', 'Prayagraj', 'Ghaziabad']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Buxar vs Prayagraj vs Ghaziabad – 2024', width=550, height=320)
     return chart


</code>
"
313,spatial_aggregation,Show a bar chart of the top 7 cities by median PM2.5 in 2017.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2017]
    df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
    df = df.nlargest(7, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
        y=alt.Y('city:N', sort='-x', title='City'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
        tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 7 Cities by Median PM2.5 in 2017', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2017]
     df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
     df = df.nlargest(7, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
         y=alt.Y('city:N', sort='-x', title='City'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
         tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 7 Cities by Median PM2.5 in 2017', width=500, height=300)
     return chart


</code>
"
314,temporal_aggregation,Plot the monthly average PM2.5 trend for Madhya Pradesh from 2017 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Madhya Pradesh'].copy()
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='steelblue').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly Average PM2.5 Trend – Madhya Pradesh (2017–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Madhya Pradesh'].copy()
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='steelblue').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly Average PM2.5 Trend – Madhya Pradesh (2017–2024)', width=600, height=300)
     return chart


</code>
"
315,spatio_temporal_aggregation,Plot the yearly average PM2.5 for the top 13 most polluted states from 2017 to 2024 as a multi-line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    top5 = data.groupby('state')['PM2.5'].mean().nlargest(13).index.tolist()
    df = data[data['state'].isin(top5)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends – Top 13 Most Polluted States', width=600, height=350)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     top5 = data.groupby('state')['PM2.5'].mean().nlargest(13).index.tolist()
     df = data[data['state'].isin(top5)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends – Top 13 Most Polluted States', width=600, height=350)
     return chart


</code>
"
316,spatial_aggregation,Visualize the bottom 10 states with the lowest average PM2.5 in 2020 using a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2020]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nsmallest(10, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Bottom 10 States by Average PM2.5 in 2020', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2020]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nsmallest(10, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Bottom 10 States by Average PM2.5 in 2020', width=500, height=300)
     return chart


</code>
"
317,spatial_aggregation,"Show the top 8 states by average PM10 in 2017 as a bar chart, sorted in descending order.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2017]
    df = df.groupby('state')['PM10'].mean().reset_index().dropna()
    df = df.nlargest(8, 'PM10')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Top 8 States by Average PM10 in 2017', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2017]
     df = df.groupby('state')['PM10'].mean().reset_index().dropna()
     df = df.nlargest(8, 'PM10')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Top 8 States by Average PM10 in 2017', width=500, height=300)
     return chart


</code>
"
318,specific_pattern,Plot the rolling 30-day average PM2.5 for Tripura in 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Tripura') & (data['Timestamp'].dt.year == 2024)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Tripura 2024', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Tripura') & (data['Timestamp'].dt.year == 2024)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Tripura 2024', width=600, height=300)


</code>
"
319,spatial_aggregation,"Show the top 15 states by average PM10 in 2024 as a bar chart, sorted in descending order.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2024]
    df = df.groupby('state')['PM10'].mean().reset_index().dropna()
    df = df.nlargest(15, 'PM10')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Top 15 States by Average PM10 in 2024', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2024]
     df = df.groupby('state')['PM10'].mean().reset_index().dropna()
     df = df.nlargest(15, 'PM10')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Top 15 States by Average PM10 in 2024', width=500, height=300)
     return chart


</code>
"
320,spatial_aggregation,Visualize the bottom 7 states with the lowest average PM2.5 in 2024 using a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2024]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nsmallest(7, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Bottom 7 States by Average PM2.5 in 2024', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2024]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nsmallest(7, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Bottom 7 States by Average PM2.5 in 2024', width=500, height=300)
     return chart


</code>
"
321,area_based,"Create a bubble chart of PM2.5 vs area for each state in 2020, sized by population.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    pm = data[data['Timestamp'].dt.year == 2020].groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = pm.merge(states_data, on='state')
    chart = alt.Chart(df).mark_point(filled=True, opacity=0.7).encode(
        x=alt.X('area (km2):Q', title='Area (km²)', axis=alt.Axis(format='~s')),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        size=alt.Size('population:Q', title='Population', scale=alt.Scale(range=[50,1500])),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f'),
                 alt.Tooltip('population:Q', format=','), alt.Tooltip('area (km2):Q', format=',')]
    ).properties(title='PM2.5 vs Area (size=Population) – 2020', width=500, height=400)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     pm = data[data['Timestamp'].dt.year == 2020].groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = pm.merge(states_data, on='state')
     chart = alt.Chart(df).mark_point(filled=True, opacity=0.7).encode(
         x=alt.X('area (km2):Q', title='Area (km²)', axis=alt.Axis(format='~s')),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         size=alt.Size('population:Q', title='Population', scale=alt.Scale(range=[50,1500])),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f'),
                  alt.Tooltip('population:Q', format=','), alt.Tooltip('area (km2):Q', format=',')]
     ).properties(title='PM2.5 vs Area (size=Population) – 2020', width=500, height=400)
     return chart


</code>
"
322,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Jammu and Kashmir stations in 2019, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Jammu and Kashmir') & (data['Timestamp'].dt.year == 2019)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Jammu and Kashmir Stations 2019', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Jammu and Kashmir') & (data['Timestamp'].dt.year == 2019)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Jammu and Kashmir Stations 2019', width=450, height=350)


</code>
"
323,temporal_aggregation,Show a monthly bar chart of the number of days Kerala exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2020.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Kerala') & (data['Timestamp'].dt.year == 2020)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Kerala Exceeded WHO PM2.5 Guideline per Month – 2020', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Kerala') & (data['Timestamp'].dt.year == 2020)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Kerala Exceeded WHO PM2.5 Guideline per Month – 2020', width=500, height=300)
     return chart


</code>
"
324,specific_pattern,Show a cumulative area chart of PM2.5 readings for Katni across 2021.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Katni') & (data['Timestamp'].dt.year == 2021)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Katni 2021', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Katni') & (data['Timestamp'].dt.year == 2021)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Katni 2021', width=600, height=300)
     return chart


</code>
"
325,population_based,"Plot a scatter chart of state-level average PM2.5 versus population for 2024, with point size representing area.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    pm = data[data['Timestamp'].dt.year == 2024].groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = pm.merge(states_data, on='state')
    chart = alt.Chart(df).mark_point(filled=True, opacity=0.7).encode(
        x=alt.X('population:Q', title='Population', axis=alt.Axis(format='~s')),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        size=alt.Size('area (km2):Q', title='Area (km²)', scale=alt.Scale(range=[50,1000])),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f'),
                 alt.Tooltip('population:Q', format=','), 'area (km2):Q']
    ).properties(title='PM2.5 vs Population (size=Area) – 2024', width=500, height=400)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     pm = data[data['Timestamp'].dt.year == 2024].groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = pm.merge(states_data, on='state')
     chart = alt.Chart(df).mark_point(filled=True, opacity=0.7).encode(
         x=alt.X('population:Q', title='Population', axis=alt.Axis(format='~s')),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         size=alt.Size('area (km2):Q', title='Area (km²)', scale=alt.Scale(range=[50,1000])),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f'),
                  alt.Tooltip('population:Q', format=','), 'area (km2):Q']
     ).properties(title='PM2.5 vs Population (size=Area) – 2024', width=500, height=400)
     return chart


</code>
"
326,spatio_temporal_aggregation,Create a heatmap showing the average PM2.5 by month (x-axis) and year (y-axis) for Haryana.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Haryana'].copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_rect().encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Year:N', title='Year'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='Avg PM2.5'),
        tooltip=['Year:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Heatmap – Haryana (Month × Year)', width=500, height=280)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Haryana'].copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_rect().encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Year:N', title='Year'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='Avg PM2.5'),
         tooltip=['Year:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Heatmap – Haryana (Month × Year)', width=500, height=280)
     return chart


</code>
"
327,spatio_temporal_aggregation,Create a heatmap showing the average PM2.5 by month (x-axis) and year (y-axis) for Bihar.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Bihar'].copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_rect().encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Year:N', title='Year'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='Avg PM2.5'),
        tooltip=['Year:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Heatmap – Bihar (Month × Year)', width=500, height=280)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Bihar'].copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_rect().encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Year:N', title='Year'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='Avg PM2.5'),
         tooltip=['Year:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Heatmap – Bihar (Month × Year)', width=500, height=280)
     return chart


</code>
"
328,temporal_aggregation,Show the monthly average PM10 trend for Rairangpur from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Rairangpur'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Rairangpur (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Rairangpur'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Rairangpur (2019–2024)', width=600, height=300)
     return chart


</code>
"
329,specific_pattern,Show a cumulative area chart of PM2.5 readings for Chandigarh across 2021.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Chandigarh') & (data['Timestamp'].dt.year == 2021)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Chandigarh 2021', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Chandigarh') & (data['Timestamp'].dt.year == 2021)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Chandigarh 2021', width=600, height=300)
     return chart


</code>
"
330,spatial_aggregation,"Show the top 14 states by average PM10 in 2023 as a bar chart, sorted in descending order.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2023]
    df = df.groupby('state')['PM10'].mean().reset_index().dropna()
    df = df.nlargest(14, 'PM10')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Top 14 States by Average PM10 in 2023', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2023]
     df = df.groupby('state')['PM10'].mean().reset_index().dropna()
     df = df.nlargest(14, 'PM10')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Top 14 States by Average PM10 in 2023', width=500, height=300)
     return chart


</code>
"
331,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Himachal Pradesh, Chhattisgarh, and Meghalaya in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Himachal Pradesh', 'Chhattisgarh', 'Meghalaya']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Himachal Pradesh, Chhattisgarh, UP – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Himachal Pradesh', 'Chhattisgarh', 'Meghalaya']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Himachal Pradesh, Chhattisgarh, UP – 2023', width=550, height=320)
     return chart


</code>
"
332,spatial_aggregation,Plot the top 11 states by average PM2.5 in 2022 as a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2022]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nlargest(11, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 11 States by Average PM2.5 in 2022', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2022]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nlargest(11, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 11 States by Average PM2.5 in 2022', width=500, height=300)
     return chart


</code>
"
333,temporal_aggregation,Plot the monthly average PM2.5 trend for Odisha from 2017 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Odisha'].copy()
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='steelblue').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly Average PM2.5 Trend – Odisha (2017–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Odisha'].copy()
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='steelblue').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly Average PM2.5 Trend – Odisha (2017–2024)', width=600, height=300)
     return chart


</code>
"
334,spatio_temporal_aggregation,Plot the yearly average PM2.5 for the top 11 most polluted states from 2017 to 2024 as a multi-line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    top5 = data.groupby('state')['PM2.5'].mean().nlargest(11).index.tolist()
    df = data[data['state'].isin(top5)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends – Top 11 Most Polluted States', width=600, height=350)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     top5 = data.groupby('state')['PM2.5'].mean().nlargest(11).index.tolist()
     df = data[data['state'].isin(top5)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends – Top 11 Most Polluted States', width=600, height=350)
     return chart


</code>
"
335,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Chandigarh, Meghalaya, and Karnataka in 2021 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Chandigarh', 'Meghalaya', 'Karnataka']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Chandigarh, Meghalaya, UP – 2021', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Chandigarh', 'Meghalaya', 'Karnataka']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Chandigarh, Meghalaya, UP – 2021', width=550, height=320)
     return chart


</code>
"
336,temporal_aggregation,Show the monthly average PM2.5 for Talcher in 2022 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Talcher') & (data['Timestamp'].dt.year == 2022)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Talcher 2022', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Talcher') & (data['Timestamp'].dt.year == 2022)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Talcher 2022', width=450, height=280)


</code>
"
337,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Uttarakhand stations in 2019, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Uttarakhand') & (data['Timestamp'].dt.year == 2019)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Uttarakhand Stations 2019', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Uttarakhand') & (data['Timestamp'].dt.year == 2019)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Uttarakhand Stations 2019', width=450, height=350)


</code>
"
338,specific_pattern,Show PM2.5 exceedances above the Indian standard (60 µg/m³) per year as a bar chart for Maharashtra.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Maharashtra'].dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 60].copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby('Year')['Timestamp'].nunique().reset_index()
    df.columns = ['Year','Days Exceeded']
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('Days Exceeded:Q', title='Days PM2.5 > 60 µg/m³'),
        tooltip=['Year:N','Days Exceeded:Q']
    ).properties(title='Days Maharashtra Exceeded Indian PM2.5 Standard (60 µg/m³) per Year', width=450, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Maharashtra'].dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 60].copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby('Year')['Timestamp'].nunique().reset_index()
     df.columns = ['Year','Days Exceeded']
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('Days Exceeded:Q', title='Days PM2.5 > 60 µg/m³'),
         tooltip=['Year:N','Days Exceeded:Q']
     ).properties(title='Days Maharashtra Exceeded Indian PM2.5 Standard (60 µg/m³) per Year', width=450, height=300)
     return chart


</code>
"
339,spatio_temporal_aggregation,Plot a heatmap of average PM10 by state (y-axis) and month (x-axis) for 2022.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2022].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['state','Month'])['PM10'].mean().reset_index().dropna()
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_rect().encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('state:N', title='State'),
        color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), title='Avg PM10'),
        tooltip=['state:N','MonthName:N', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='PM10 Heatmap by State and Month – 2022', width=500, height=400)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2022].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['state','Month'])['PM10'].mean().reset_index().dropna()
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_rect().encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('state:N', title='State'),
         color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), title='Avg PM10'),
         tooltip=['state:N','MonthName:N', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='PM10 Heatmap by State and Month – 2022', width=500, height=400)
     return chart


</code>
"
340,temporal_aggregation,Show the monthly average PM2.5 for Jaipur in 2023 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Jaipur') & (data['Timestamp'].dt.year == 2023)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Jaipur 2023', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Jaipur') & (data['Timestamp'].dt.year == 2023)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Jaipur 2023', width=450, height=280)


</code>
"
341,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Tripura, Kerala, and Sikkim from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Tripura', 'Kerala', 'Sikkim']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Tripura vs Kerala vs Sikkim', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Tripura', 'Kerala', 'Sikkim']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Tripura vs Kerala vs Sikkim', width=550, height=320)
     return chart


</code>
"
342,spatial_aggregation,Visualize the bottom 12 states with the lowest average PM2.5 in 2019 using a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2019]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nsmallest(12, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Bottom 12 States by Average PM2.5 in 2019', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2019]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nsmallest(12, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Bottom 12 States by Average PM2.5 in 2019', width=500, height=300)
     return chart


</code>
"
343,specific_pattern,Show PM2.5 exceedances above the Indian standard (60 µg/m³) per year as a bar chart for Tamil Nadu.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Tamil Nadu'].dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 60].copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby('Year')['Timestamp'].nunique().reset_index()
    df.columns = ['Year','Days Exceeded']
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('Days Exceeded:Q', title='Days PM2.5 > 60 µg/m³'),
        tooltip=['Year:N','Days Exceeded:Q']
    ).properties(title='Days Tamil Nadu Exceeded Indian PM2.5 Standard (60 µg/m³) per Year', width=450, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Tamil Nadu'].dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 60].copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby('Year')['Timestamp'].nunique().reset_index()
     df.columns = ['Year','Days Exceeded']
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('Days Exceeded:Q', title='Days PM2.5 > 60 µg/m³'),
         tooltip=['Year:N','Days Exceeded:Q']
     ).properties(title='Days Tamil Nadu Exceeded Indian PM2.5 Standard (60 µg/m³) per Year', width=450, height=300)
     return chart


</code>
"
344,temporal_aggregation,Show the monthly average PM10 trend for Bikaner from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Bikaner'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Bikaner (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Bikaner'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Bikaner (2019–2024)', width=600, height=300)
     return chart


</code>
"
345,spatial_aggregation,Plot the top 7 states by average PM2.5 in 2023 as a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2023]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nlargest(7, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 7 States by Average PM2.5 in 2023', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2023]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nlargest(7, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 7 States by Average PM2.5 in 2023', width=500, height=300)
     return chart


</code>
"
346,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Odisha stations in 2024, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Odisha') & (data['Timestamp'].dt.year == 2024)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Odisha Stations 2024', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Odisha') & (data['Timestamp'].dt.year == 2024)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Odisha Stations 2024', width=450, height=350)


</code>
"
347,spatial_aggregation,Show a bar chart of the top 9 cities by median PM2.5 in 2020.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2020]
    df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
    df = df.nlargest(9, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
        y=alt.Y('city:N', sort='-x', title='City'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
        tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 9 Cities by Median PM2.5 in 2020', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2020]
     df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
     df = df.nlargest(9, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
         y=alt.Y('city:N', sort='-x', title='City'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
         tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 9 Cities by Median PM2.5 in 2020', width=500, height=300)
     return chart


</code>
"
348,spatial_aggregation,Show a bar chart of the top 15 cities by median PM2.5 in 2018.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2018]
    df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
    df = df.nlargest(15, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
        y=alt.Y('city:N', sort='-x', title='City'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
        tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 15 Cities by Median PM2.5 in 2018', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2018]
     df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
     df = df.nlargest(15, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
         y=alt.Y('city:N', sort='-x', title='City'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
         tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 15 Cities by Median PM2.5 in 2018', width=500, height=300)
     return chart


</code>
"
349,specific_pattern,Plot the rolling 30-day average PM2.5 for Gujarat in 2019 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Gujarat') & (data['Timestamp'].dt.year == 2019)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Gujarat 2019', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Gujarat') & (data['Timestamp'].dt.year == 2019)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Gujarat 2019', width=600, height=300)


</code>
"
350,temporal_aggregation,Show a monthly bar chart of the number of days Himachal Pradesh exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2023.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Himachal Pradesh') & (data['Timestamp'].dt.year == 2023)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Himachal Pradesh Exceeded WHO PM2.5 Guideline per Month – 2023', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Himachal Pradesh') & (data['Timestamp'].dt.year == 2023)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Himachal Pradesh Exceeded WHO PM2.5 Guideline per Month – 2023', width=500, height=300)
     return chart


</code>
"
351,spatial_aggregation,"Show the top 10 states by average PM10 in 2017 as a bar chart, sorted in descending order.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2017]
    df = df.groupby('state')['PM10'].mean().reset_index().dropna()
    df = df.nlargest(10, 'PM10')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Top 10 States by Average PM10 in 2017', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2017]
     df = df.groupby('state')['PM10'].mean().reset_index().dropna()
     df = df.nlargest(10, 'PM10')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Top 10 States by Average PM10 in 2017', width=500, height=300)
     return chart


</code>
"
352,spatio_temporal_aggregation,Show a heatmap of average PM2.5 for the top 7 most polluted states by month for 2021.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    top8 = data.groupby('state')['PM2.5'].mean().nlargest(7).index.tolist()
    df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2021)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_rect().encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('state:N', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
        tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Heatmap – Top 7 Polluted States by Month (2021)', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     top8 = data.groupby('state')['PM2.5'].mean().nlargest(7).index.tolist()
     df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2021)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_rect().encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('state:N', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
         tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Heatmap – Top 7 Polluted States by Month (2021)', width=500, height=300)
     return chart


</code>
"
353,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Mizoram, Kerala, and Puducherry in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Mizoram', 'Kerala', 'Puducherry']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Mizoram, Kerala, UP – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Mizoram', 'Kerala', 'Puducherry']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Mizoram, Kerala, UP – 2022', width=550, height=320)
     return chart


</code>
"
354,spatial_aggregation,"Create a scatter plot of PM2.5 vs PM10 for all stations in 2022, colored by state.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2022]
    df = df.groupby(['station','state'])[['PM2.5','PM10']].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_point(opacity=0.6, size=60).encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['station:N','state:N',
                 alt.Tooltip('PM2\.5:Q', format='.1f'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='PM2.5 vs PM10 by Station – 2022', width=500, height=400)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2022]
     df = df.groupby(['station','state'])[['PM2.5','PM10']].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_point(opacity=0.6, size=60).encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['station:N','state:N',
                  alt.Tooltip('PM2\.5:Q', format='.1f'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='PM2.5 vs PM10 by Station – 2022', width=500, height=400)
     return chart


</code>
"
355,specific_pattern,Plot the rolling 30-day average PM2.5 for Maharashtra in 2018 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Maharashtra') & (data['Timestamp'].dt.year == 2018)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Maharashtra 2018', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Maharashtra') & (data['Timestamp'].dt.year == 2018)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Maharashtra 2018', width=600, height=300)


</code>
"
356,area_based,"Create a bubble chart of PM2.5 vs area for each state in 2018, sized by population.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    pm = data[data['Timestamp'].dt.year == 2018].groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = pm.merge(states_data, on='state')
    chart = alt.Chart(df).mark_point(filled=True, opacity=0.7).encode(
        x=alt.X('area (km2):Q', title='Area (km²)', axis=alt.Axis(format='~s')),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        size=alt.Size('population:Q', title='Population', scale=alt.Scale(range=[50,1500])),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f'),
                 alt.Tooltip('population:Q', format=','), alt.Tooltip('area (km2):Q', format=',')]
    ).properties(title='PM2.5 vs Area (size=Population) – 2018', width=500, height=400)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     pm = data[data['Timestamp'].dt.year == 2018].groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = pm.merge(states_data, on='state')
     chart = alt.Chart(df).mark_point(filled=True, opacity=0.7).encode(
         x=alt.X('area (km2):Q', title='Area (km²)', axis=alt.Axis(format='~s')),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         size=alt.Size('population:Q', title='Population', scale=alt.Scale(range=[50,1500])),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f'),
                  alt.Tooltip('population:Q', format=','), alt.Tooltip('area (km2):Q', format=',')]
     ).properties(title='PM2.5 vs Area (size=Population) – 2018', width=500, height=400)
     return chart


</code>
"
357,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Madhya Pradesh, Haryana, and Odisha from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Madhya Pradesh', 'Haryana', 'Odisha']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Madhya Pradesh vs Haryana vs Odisha', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Madhya Pradesh', 'Haryana', 'Odisha']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Madhya Pradesh vs Haryana vs Odisha', width=550, height=320)
     return chart


</code>
"
358,specific_pattern,Plot the rolling 30-day average PM2.5 for Chhattisgarh in 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Chhattisgarh') & (data['Timestamp'].dt.year == 2022)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Chhattisgarh 2022', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Chhattisgarh') & (data['Timestamp'].dt.year == 2022)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Chhattisgarh 2022', width=600, height=300)


</code>
"
359,spatial_aggregation,Show a bar chart of the top 10 cities by median PM2.5 in 2020.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2020]
    df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
    df = df.nlargest(10, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
        y=alt.Y('city:N', sort='-x', title='City'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
        tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 10 Cities by Median PM2.5 in 2020', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2020]
     df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
     df = df.nlargest(10, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
         y=alt.Y('city:N', sort='-x', title='City'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
         tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 10 Cities by Median PM2.5 in 2020', width=500, height=300)
     return chart


</code>
"
360,spatial_aggregation,Plot the distribution of PM2.5 values in West Bengal across all years using a histogram.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'West Bengal'].dropna(subset=['PM2.5'])
    df = df[['PM2.5']]
    chart = alt.Chart(df).mark_bar(color='steelblue', opacity=0.75).encode(
        x=alt.X('PM2\.5:Q', bin=alt.Bin(maxbins=40), title='PM2.5 (µg/m³)'),
        y=alt.Y('count()', title='Number of Observations'),
        tooltip=[alt.Tooltip('PM2\.5:Q', bin=True), 'count()']
    ).properties(title='PM2.5 Distribution – West Bengal (All Years)', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'West Bengal'].dropna(subset=['PM2.5'])
     df = df[['PM2.5']]
     chart = alt.Chart(df).mark_bar(color='steelblue', opacity=0.75).encode(
         x=alt.X('PM2\.5:Q', bin=alt.Bin(maxbins=40), title='PM2.5 (µg/m³)'),
         y=alt.Y('count()', title='Number of Observations'),
         tooltip=[alt.Tooltip('PM2\.5:Q', bin=True), 'count()']
     ).properties(title='PM2.5 Distribution – West Bengal (All Years)', width=500, height=300)
     return chart


</code>
"
361,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Madhya Pradesh, Himachal Pradesh, and Tripura across 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Madhya Pradesh', 'Himachal Pradesh', 'Tripura']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Madhya Pradesh', 'Himachal Pradesh', 'Tripura']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
     return chart


</code>
"
362,temporal_aggregation,Show the monthly average PM2.5 for Silchar in 2020 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Silchar') & (data['Timestamp'].dt.year == 2020)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Silchar 2020', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Silchar') & (data['Timestamp'].dt.year == 2020)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Silchar 2020', width=450, height=280)


</code>
"
363,specific_pattern,Show a cumulative area chart of PM2.5 readings for Amritsar across 2019.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Amritsar') & (data['Timestamp'].dt.year == 2019)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Amritsar 2019', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Amritsar') & (data['Timestamp'].dt.year == 2019)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Amritsar 2019', width=600, height=300)
     return chart


</code>
"
364,specific_pattern,Show PM2.5 exceedances above the Indian standard (60 µg/m³) per year as a bar chart for Karnataka.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Karnataka'].dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 60].copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby('Year')['Timestamp'].nunique().reset_index()
    df.columns = ['Year','Days Exceeded']
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('Days Exceeded:Q', title='Days PM2.5 > 60 µg/m³'),
        tooltip=['Year:N','Days Exceeded:Q']
    ).properties(title='Days Karnataka Exceeded Indian PM2.5 Standard (60 µg/m³) per Year', width=450, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Karnataka'].dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 60].copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby('Year')['Timestamp'].nunique().reset_index()
     df.columns = ['Year','Days Exceeded']
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('Days Exceeded:Q', title='Days PM2.5 > 60 µg/m³'),
         tooltip=['Year:N','Days Exceeded:Q']
     ).properties(title='Days Karnataka Exceeded Indian PM2.5 Standard (60 µg/m³) per Year', width=450, height=300)
     return chart


</code>
"
365,area_based,Show the PM2.5 per 1000 km² (air quality density) for each state in 2024 as a bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    pm = data[data['Timestamp'].dt.year == 2024].groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = pm.merge(states_data[['state','area (km2)']], on='state')
    df['PM2.5 per 1000 km²'] = df['PM2.5'] / df['area (km2)'] * 1000
    df = df.sort_values('PM2.5 per 1000 km²', ascending=False)
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5 per 1000 km²:Q', title='PM2.5 per 1000 km²'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM2\.5 per 1000 km²:Q', scale=alt.Scale(scheme='orangered'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5 per 1000 km²:Q', format='.3f')]
    ).properties(title='Air Quality Density (PM2.5 per 1000 km²) by State – 2024', width=500, height=400)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     pm = data[data['Timestamp'].dt.year == 2024].groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = pm.merge(states_data[['state','area (km2)']], on='state')
     df['PM2.5 per 1000 km²'] = df['PM2.5'] / df['area (km2)'] * 1000
     df = df.sort_values('PM2.5 per 1000 km²', ascending=False)
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5 per 1000 km²:Q', title='PM2.5 per 1000 km²'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM2\.5 per 1000 km²:Q', scale=alt.Scale(scheme='orangered'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5 per 1000 km²:Q', format='.3f')]
     ).properties(title='Air Quality Density (PM2.5 per 1000 km²) by State – 2024', width=500, height=400)
     return chart


</code>
"
366,specific_pattern,Plot the rolling 30-day average PM2.5 for Rajasthan in 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Rajasthan') & (data['Timestamp'].dt.year == 2024)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Rajasthan 2024', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Rajasthan') & (data['Timestamp'].dt.year == 2024)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Rajasthan 2024', width=600, height=300)


</code>
"
367,spatio_temporal_aggregation,Show the monthly average PM2.5 for Sikkim across each year from 2017 to 2024 as small multiples (faceted by year).,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Sikkim'].copy()
    df['Year'] = df['Timestamp'].dt.year
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='steelblue').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5'),
        tooltip=['Year:O','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).facet(
        facet=alt.Facet('Year:O', title='Year'),
        columns=4
    ).properties(title='Monthly PM2.5 in Sikkim by Year (2017–2024)')
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Sikkim'].copy()
     df['Year'] = df['Timestamp'].dt.year
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='steelblue').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5'),
         tooltip=['Year:O','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).facet(
         facet=alt.Facet('Year:O', title='Year'),
         columns=4
     ).properties(title='Monthly PM2.5 in Sikkim by Year (2017–2024)')
     return chart


</code>
"
368,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Nagaland, Jharkhand, and Tamil Nadu from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Nagaland', 'Jharkhand', 'Tamil Nadu']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Nagaland vs Jharkhand vs Tamil Nadu', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Nagaland', 'Jharkhand', 'Tamil Nadu']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Nagaland vs Jharkhand vs Tamil Nadu', width=550, height=320)
     return chart


</code>
"
369,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Odisha, Nagaland, and Delhi from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Odisha', 'Nagaland', 'Delhi']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Odisha vs Nagaland vs Delhi', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Odisha', 'Nagaland', 'Delhi']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Odisha vs Nagaland vs Delhi', width=550, height=320)
     return chart


</code>
"
370,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Baddi, Bidar, and Chikkamagaluru in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Baddi', 'Bidar', 'Chikkamagaluru']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Baddi vs Bidar vs Chikkamagaluru – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Baddi', 'Bidar', 'Chikkamagaluru']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Baddi vs Bidar vs Chikkamagaluru – 2023', width=550, height=320)
     return chart


</code>
"
371,spatio_temporal_aggregation,"Create a faceted bar chart showing top 5 states by average PM2.5 per year for 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year.isin([2021,2022,2023,2024])].copy()
    df['Year'] = df['Timestamp'].dt.year
    agg = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    top = agg.groupby('Year').apply(lambda x: x.nlargest(5,'PM2.5')).reset_index(drop=True)
    chart = alt.Chart(top).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Avg PM2.5'),
        y=alt.Y('state:N', sort='-x'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).facet(facet='Year:O', columns=2).properties(title='Top 5 States by PM2.5 per Year')
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year.isin([2021,2022,2023,2024])].copy()
     df['Year'] = df['Timestamp'].dt.year
     agg = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     top = agg.groupby('Year').apply(lambda x: x.nlargest(5,'PM2.5')).reset_index(drop=True)
     chart = alt.Chart(top).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Avg PM2.5'),
         y=alt.Y('state:N', sort='-x'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).facet(facet='Year:O', columns=2).properties(title='Top 5 States by PM2.5 per Year')
     return chart


</code>
"
372,temporal_aggregation,Show the monthly average PM10 trend for Kishanganj from 2017 to 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Kishanganj'].copy()
    df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Kishanganj (2017–2022)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Kishanganj'].copy()
     df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Kishanganj (2017–2022)', width=600, height=300)
     return chart


</code>
"
373,spatio_temporal_aggregation,Create a heatmap showing the average PM2.5 by month (x-axis) and year (y-axis) for Rajasthan.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Rajasthan'].copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_rect().encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Year:N', title='Year'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='Avg PM2.5'),
        tooltip=['Year:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Heatmap – Rajasthan (Month × Year)', width=500, height=280)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Rajasthan'].copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_rect().encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Year:N', title='Year'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='Avg PM2.5'),
         tooltip=['Year:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Heatmap – Rajasthan (Month × Year)', width=500, height=280)
     return chart


</code>
"
374,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Delhi stations in 2018, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Delhi') & (data['Timestamp'].dt.year == 2018)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Delhi Stations 2018', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Delhi') & (data['Timestamp'].dt.year == 2018)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Delhi Stations 2018', width=450, height=350)


</code>
"
375,temporal_aggregation,Show the monthly average PM10 trend for Tonk from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Tonk'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Tonk (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Tonk'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Tonk (2019–2024)', width=600, height=300)
     return chart


</code>
"
376,spatial_aggregation,Show a bar chart of the top 8 cities by median PM2.5 in 2018.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2018]
    df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
    df = df.nlargest(8, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
        y=alt.Y('city:N', sort='-x', title='City'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
        tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 8 Cities by Median PM2.5 in 2018', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2018]
     df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
     df = df.nlargest(8, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
         y=alt.Y('city:N', sort='-x', title='City'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
         tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 8 Cities by Median PM2.5 in 2018', width=500, height=300)
     return chart


</code>
"
377,specific_pattern,Plot the rolling 30-day average PM2.5 for Puducherry in 2023 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Puducherry') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Puducherry 2023', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Puducherry') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Puducherry 2023', width=600, height=300)


</code>
"
378,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Mysuru, Rishikesh, and Chandrapur in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Mysuru', 'Rishikesh', 'Chandrapur']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Mysuru vs Rishikesh vs Chandrapur – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Mysuru', 'Rishikesh', 'Chandrapur']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Mysuru vs Rishikesh vs Chandrapur – 2022', width=550, height=320)
     return chart


</code>
"
379,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Brajrajnagar, Nagapattinam, and Nanded in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Brajrajnagar', 'Nagapattinam', 'Nanded']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Brajrajnagar vs Nagapattinam vs Nanded – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Brajrajnagar', 'Nagapattinam', 'Nanded']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Brajrajnagar vs Nagapattinam vs Nanded – 2022', width=550, height=320)
     return chart


</code>
"
380,spatial_aggregation,"Show the top 13 states by average PM10 in 2022 as a bar chart, sorted in descending order.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2022]
    df = df.groupby('state')['PM10'].mean().reset_index().dropna()
    df = df.nlargest(13, 'PM10')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Top 13 States by Average PM10 in 2022', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2022]
     df = df.groupby('state')['PM10'].mean().reset_index().dropna()
     df = df.nlargest(13, 'PM10')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Top 13 States by Average PM10 in 2022', width=500, height=300)
     return chart


</code>
"
381,spatial_aggregation,Show a bar chart of the top 12 cities by median PM2.5 in 2023.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2023]
    df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
    df = df.nlargest(12, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
        y=alt.Y('city:N', sort='-x', title='City'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
        tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 12 Cities by Median PM2.5 in 2023', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2023]
     df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
     df = df.nlargest(12, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
         y=alt.Y('city:N', sort='-x', title='City'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
         tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 12 Cities by Median PM2.5 in 2023', width=500, height=300)
     return chart


</code>
"
382,spatial_aggregation,Visualize the bottom 9 states with the lowest average PM2.5 in 2017 using a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2017]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nsmallest(9, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Bottom 9 States by Average PM2.5 in 2017', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2017]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nsmallest(9, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Bottom 9 States by Average PM2.5 in 2017', width=500, height=300)
     return chart


</code>
"
383,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Bihar stations in 2023, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Bihar') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Bihar Stations 2023', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Bihar') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Bihar Stations 2023', width=450, height=350)


</code>
"
384,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Arunachal Pradesh stations in 2019, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Arunachal Pradesh') & (data['Timestamp'].dt.year == 2019)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Arunachal Pradesh Stations 2019', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Arunachal Pradesh') & (data['Timestamp'].dt.year == 2019)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Arunachal Pradesh Stations 2019', width=450, height=350)


</code>
"
385,specific_pattern,Show a cumulative area chart of PM2.5 readings for Hapur across 2019.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Hapur') & (data['Timestamp'].dt.year == 2019)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Hapur 2019', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Hapur') & (data['Timestamp'].dt.year == 2019)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Hapur 2019', width=600, height=300)
     return chart


</code>
"
386,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Bihar, Jharkhand, and Puducherry in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Bihar', 'Jharkhand', 'Puducherry']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Bihar, Jharkhand, UP – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Bihar', 'Jharkhand', 'Puducherry']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Bihar, Jharkhand, UP – 2024', width=550, height=320)
     return chart


</code>
"
387,spatio_temporal_aggregation,Show a heatmap of average PM2.5 for the top 15 most polluted states by month for 2022.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    top8 = data.groupby('state')['PM2.5'].mean().nlargest(15).index.tolist()
    df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_rect().encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('state:N', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
        tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Heatmap – Top 15 Polluted States by Month (2022)', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     top8 = data.groupby('state')['PM2.5'].mean().nlargest(15).index.tolist()
     df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_rect().encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('state:N', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
         tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Heatmap – Top 15 Polluted States by Month (2022)', width=500, height=300)
     return chart


</code>
"
388,spatial_aggregation,Plot the average PM2.5 across all states in February 2021 as a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['Timestamp'].dt.year == 2021) & (data['Timestamp'].dt.month == 2)]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('PM2.5', ascending=False)
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='plasma'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Average PM2.5 by State – February 2021', width=500, height=400)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['Timestamp'].dt.year == 2021) & (data['Timestamp'].dt.month == 2)]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('PM2.5', ascending=False)
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='plasma'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Average PM2.5 by State – February 2021', width=500, height=400)
     return chart


</code>
"
389,spatio_temporal_aggregation,Plot the yearly average PM2.5 for the top 8 most polluted states from 2017 to 2024 as a multi-line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    top5 = data.groupby('state')['PM2.5'].mean().nlargest(8).index.tolist()
    df = data[data['state'].isin(top5)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends – Top 8 Most Polluted States', width=600, height=350)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     top5 = data.groupby('state')['PM2.5'].mean().nlargest(8).index.tolist()
     df = data[data['state'].isin(top5)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends – Top 8 Most Polluted States', width=600, height=350)
     return chart


</code>
"
390,temporal_aggregation,Plot the monthly average PM2.5 trend for West Bengal from 2017 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'West Bengal'].copy()
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='steelblue').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly Average PM2.5 Trend – West Bengal (2017–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'West Bengal'].copy()
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='steelblue').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly Average PM2.5 Trend – West Bengal (2017–2024)', width=600, height=300)
     return chart


</code>
"
391,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Mizoram, Tamil Nadu, and Delhi in 2018 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Mizoram', 'Tamil Nadu', 'Delhi']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2018)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Mizoram, Tamil Nadu, UP – 2018', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Mizoram', 'Tamil Nadu', 'Delhi']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2018)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Mizoram, Tamil Nadu, UP – 2018', width=550, height=320)
     return chart


</code>
"
392,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Jammu and Kashmir, Chandigarh, and Sikkim from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Jammu and Kashmir', 'Chandigarh', 'Sikkim']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Jammu and Kashmir vs Chandigarh vs Sikkim', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Jammu and Kashmir', 'Chandigarh', 'Sikkim']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Jammu and Kashmir vs Chandigarh vs Sikkim', width=550, height=320)
     return chart


</code>
"
393,population_based,Bar chart of PM2.5 per capita (average PM2.5 × 1000 / population) for each state in 2017.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    pm = data[data['Timestamp'].dt.year == 2017].groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = pm.merge(states_data[['state','population']], on='state')
    df['PM2.5 per Capita (×1000)'] = df['PM2.5'] / df['population'] * 1e6
    df = df.sort_values('PM2.5 per Capita (×1000)', ascending=False)
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5 per Capita (×1000):Q', title='PM2.5 per Million Population'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM2\.5 per Capita (×1000):Q', scale=alt.Scale(scheme='purples'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5 per Capita (×1000):Q', format='.4f')]
    ).properties(title='PM2.5 Per-Capita Pollution Index by State – 2017', width=500, height=400)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     pm = data[data['Timestamp'].dt.year == 2017].groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = pm.merge(states_data[['state','population']], on='state')
     df['PM2.5 per Capita (×1000)'] = df['PM2.5'] / df['population'] * 1e6
     df = df.sort_values('PM2.5 per Capita (×1000)', ascending=False)
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5 per Capita (×1000):Q', title='PM2.5 per Million Population'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM2\.5 per Capita (×1000):Q', scale=alt.Scale(scheme='purples'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5 per Capita (×1000):Q', format='.4f')]
     ).properties(title='PM2.5 Per-Capita Pollution Index by State – 2017', width=500, height=400)
     return chart


</code>
"
394,temporal_aggregation,Show the monthly average PM10 trend for Dindigul from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Dindigul'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Dindigul (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Dindigul'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Dindigul (2019–2024)', width=600, height=300)
     return chart


</code>
"
395,temporal_aggregation,Plot the monthly average PM2.5 trend for Puducherry from 2017 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Puducherry'].copy()
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='steelblue').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly Average PM2.5 Trend – Puducherry (2017–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Puducherry'].copy()
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='steelblue').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly Average PM2.5 Trend – Puducherry (2017–2024)', width=600, height=300)
     return chart


</code>
"
396,spatial_aggregation,"Show the top 5 states by average PM10 in 2018 as a bar chart, sorted in descending order.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2018]
    df = df.groupby('state')['PM10'].mean().reset_index().dropna()
    df = df.nlargest(5, 'PM10')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Top 5 States by Average PM10 in 2018', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2018]
     df = df.groupby('state')['PM10'].mean().reset_index().dropna()
     df = df.nlargest(5, 'PM10')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Top 5 States by Average PM10 in 2018', width=500, height=300)
     return chart


</code>
"
397,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Andhra Pradesh, Jammu and Kashmir, and Chhattisgarh in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Andhra Pradesh', 'Jammu and Kashmir', 'Chhattisgarh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Andhra Pradesh, Jammu and Kashmir, UP – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Andhra Pradesh', 'Jammu and Kashmir', 'Chhattisgarh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Andhra Pradesh, Jammu and Kashmir, UP – 2022', width=550, height=320)
     return chart


</code>
"
398,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Tirupati, Pithampur, and Charkhi Dadri in 2017 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Tirupati', 'Pithampur', 'Charkhi Dadri']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2017)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Tirupati vs Pithampur vs Charkhi Dadri – 2017', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Tirupati', 'Pithampur', 'Charkhi Dadri']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2017)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Tirupati vs Pithampur vs Charkhi Dadri – 2017', width=550, height=320)
     return chart


</code>
"
399,spatio_temporal_aggregation,Show a heatmap of average PM2.5 for the top 14 most polluted states by month for 2022.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    top8 = data.groupby('state')['PM2.5'].mean().nlargest(14).index.tolist()
    df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_rect().encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('state:N', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
        tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Heatmap – Top 14 Polluted States by Month (2022)', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     top8 = data.groupby('state')['PM2.5'].mean().nlargest(14).index.tolist()
     df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_rect().encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('state:N', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
         tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Heatmap – Top 14 Polluted States by Month (2022)', width=500, height=300)
     return chart


</code>
"
400,spatial_aggregation,Show a box plot of PM2.5 distribution for each state in 2017.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2017].dropna(subset=['PM2.5'])
    df = df[['state','PM2.5']]
    state_order = df.groupby('state')['PM2.5'].median().sort_values(ascending=False).index.tolist()
    chart = alt.Chart(df).mark_boxplot(extent='min-max').encode(
        x=alt.X('PM2\.5:Q', title='PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort=state_order, title='State'),
        color=alt.Color('state:N', legend=None)
    ).properties(title='PM2.5 Distribution by State – 2017', width=500, height=450)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2017].dropna(subset=['PM2.5'])
     df = df[['state','PM2.5']]
     state_order = df.groupby('state')['PM2.5'].median().sort_values(ascending=False).index.tolist()
     chart = alt.Chart(df).mark_boxplot(extent='min-max').encode(
         x=alt.X('PM2\.5:Q', title='PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort=state_order, title='State'),
         color=alt.Color('state:N', legend=None)
     ).properties(title='PM2.5 Distribution by State – 2017', width=500, height=450)
     return chart


</code>
"
401,specific_pattern,Show PM2.5 exceedances above the Indian standard (60 µg/m³) per year as a bar chart for Manipur.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Manipur'].dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 60].copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby('Year')['Timestamp'].nunique().reset_index()
    df.columns = ['Year','Days Exceeded']
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('Days Exceeded:Q', title='Days PM2.5 > 60 µg/m³'),
        tooltip=['Year:N','Days Exceeded:Q']
    ).properties(title='Days Manipur Exceeded Indian PM2.5 Standard (60 µg/m³) per Year', width=450, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Manipur'].dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 60].copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby('Year')['Timestamp'].nunique().reset_index()
     df.columns = ['Year','Days Exceeded']
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('Days Exceeded:Q', title='Days PM2.5 > 60 µg/m³'),
         tooltip=['Year:N','Days Exceeded:Q']
     ).properties(title='Days Manipur Exceeded Indian PM2.5 Standard (60 µg/m³) per Year', width=450, height=300)
     return chart


</code>
"
402,spatio_temporal_aggregation,"Create a faceted bar chart showing top 15 states by average PM2.5 per year for 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year.isin([2021,2022,2023,2024])].copy()
    df['Year'] = df['Timestamp'].dt.year
    agg = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    top = agg.groupby('Year').apply(lambda x: x.nlargest(15,'PM2.5')).reset_index(drop=True)
    chart = alt.Chart(top).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Avg PM2.5'),
        y=alt.Y('state:N', sort='-x'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).facet(facet='Year:O', columns=2).properties(title='Top 15 States by PM2.5 per Year')
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year.isin([2021,2022,2023,2024])].copy()
     df['Year'] = df['Timestamp'].dt.year
     agg = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     top = agg.groupby('Year').apply(lambda x: x.nlargest(15,'PM2.5')).reset_index(drop=True)
     chart = alt.Chart(top).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Avg PM2.5'),
         y=alt.Y('state:N', sort='-x'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).facet(facet='Year:O', columns=2).properties(title='Top 15 States by PM2.5 per Year')
     return chart


</code>
"
403,spatial_aggregation,"Show the top 12 states by average PM10 in 2017 as a bar chart, sorted in descending order.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2017]
    df = df.groupby('state')['PM10'].mean().reset_index().dropna()
    df = df.nlargest(12, 'PM10')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Top 12 States by Average PM10 in 2017', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2017]
     df = df.groupby('state')['PM10'].mean().reset_index().dropna()
     df = df.nlargest(12, 'PM10')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Top 12 States by Average PM10 in 2017', width=500, height=300)
     return chart


</code>
"
404,population_based,"Plot a scatter chart of state-level average PM2.5 versus population for 2021, with point size representing area.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    pm = data[data['Timestamp'].dt.year == 2021].groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = pm.merge(states_data, on='state')
    chart = alt.Chart(df).mark_point(filled=True, opacity=0.7).encode(
        x=alt.X('population:Q', title='Population', axis=alt.Axis(format='~s')),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        size=alt.Size('area (km2):Q', title='Area (km²)', scale=alt.Scale(range=[50,1000])),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f'),
                 alt.Tooltip('population:Q', format=','), 'area (km2):Q']
    ).properties(title='PM2.5 vs Population (size=Area) – 2021', width=500, height=400)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     pm = data[data['Timestamp'].dt.year == 2021].groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = pm.merge(states_data, on='state')
     chart = alt.Chart(df).mark_point(filled=True, opacity=0.7).encode(
         x=alt.X('population:Q', title='Population', axis=alt.Axis(format='~s')),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         size=alt.Size('area (km2):Q', title='Area (km²)', scale=alt.Scale(range=[50,1000])),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f'),
                  alt.Tooltip('population:Q', format=','), 'area (km2):Q']
     ).properties(title='PM2.5 vs Population (size=Area) – 2021', width=500, height=400)
     return chart


</code>
"
405,specific_pattern,Plot the rolling 30-day average PM2.5 for Maharashtra in 2017 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Maharashtra') & (data['Timestamp'].dt.year == 2017)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Maharashtra 2017', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Maharashtra') & (data['Timestamp'].dt.year == 2017)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Maharashtra 2017', width=600, height=300)


</code>
"
406,spatial_aggregation,Plot the median PM10 for each state in summer 2019 (April–June) as a sorted bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['Timestamp'].dt.year == 2019) & (data['Timestamp'].dt.month.isin([4,5,6]))]
    df = df.groupby('state')['PM10'].median().reset_index().dropna()
    df = df.sort_values('PM10', ascending=False)
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM10:Q', title='Median PM10 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM10:Q', scale=alt.Scale(scheme='yelloworangered'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Median PM10 by State – Summer 2019 (Apr–Jun)', width=500, height=400)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['Timestamp'].dt.year == 2019) & (data['Timestamp'].dt.month.isin([4,5,6]))]
     df = df.groupby('state')['PM10'].median().reset_index().dropna()
     df = df.sort_values('PM10', ascending=False)
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM10:Q', title='Median PM10 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM10:Q', scale=alt.Scale(scheme='yelloworangered'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Median PM10 by State – Summer 2019 (Apr–Jun)', width=500, height=400)
     return chart


</code>
"
407,spatial_aggregation,Show a bar chart of the top 9 cities by median PM2.5 in 2018.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2018]
    df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
    df = df.nlargest(9, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
        y=alt.Y('city:N', sort='-x', title='City'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
        tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 9 Cities by Median PM2.5 in 2018', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2018]
     df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
     df = df.nlargest(9, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
         y=alt.Y('city:N', sort='-x', title='City'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
         tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 9 Cities by Median PM2.5 in 2018', width=500, height=300)
     return chart


</code>
"
408,spatio_temporal_aggregation,Create a heatmap showing the average PM2.5 by month (x-axis) and year (y-axis) for Karnataka.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Karnataka'].copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_rect().encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Year:N', title='Year'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='Avg PM2.5'),
        tooltip=['Year:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Heatmap – Karnataka (Month × Year)', width=500, height=280)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Karnataka'].copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_rect().encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Year:N', title='Year'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='Avg PM2.5'),
         tooltip=['Year:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Heatmap – Karnataka (Month × Year)', width=500, height=280)
     return chart


</code>
"
409,spatio_temporal_aggregation,Show the monthly average PM2.5 for Madhya Pradesh across each year from 2017 to 2024 as small multiples (faceted by year).,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Madhya Pradesh'].copy()
    df['Year'] = df['Timestamp'].dt.year
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='steelblue').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5'),
        tooltip=['Year:O','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).facet(
        facet=alt.Facet('Year:O', title='Year'),
        columns=4
    ).properties(title='Monthly PM2.5 in Madhya Pradesh by Year (2017–2024)')
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Madhya Pradesh'].copy()
     df['Year'] = df['Timestamp'].dt.year
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='steelblue').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5'),
         tooltip=['Year:O','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).facet(
         facet=alt.Facet('Year:O', title='Year'),
         columns=4
     ).properties(title='Monthly PM2.5 in Madhya Pradesh by Year (2017–2024)')
     return chart


</code>
"
410,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Tamil Nadu, Bihar, and Punjab across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Tamil Nadu', 'Bihar', 'Punjab']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Tamil Nadu', 'Bihar', 'Punjab']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
411,population_based,"Plot average PM2.5 (2023) vs state population as a scatter plot, labeling each point with the state name.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    pm = data[data['Timestamp'].dt.year == 2023].groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = pm.merge(states_data, on='state')
    points = alt.Chart(df).mark_point(filled=True, size=80).encode(
        x=alt.X('population:Q', title='Population', axis=alt.Axis(format='~s')),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('population:Q', format=',')]
    )
    labels = alt.Chart(df).mark_text(align='left', dx=5, fontSize=9).encode(
        x='population:Q', y='PM2\.5:Q', text='state:N'
    )
    return (points + labels).properties(title='PM2.5 vs Population by State – 2023', width=500, height=400)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     pm = data[data['Timestamp'].dt.year == 2023].groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = pm.merge(states_data, on='state')
     points = alt.Chart(df).mark_point(filled=True, size=80).encode(
         x=alt.X('population:Q', title='Population', axis=alt.Axis(format='~s')),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('population:Q', format=',')]
     )
     labels = alt.Chart(df).mark_text(align='left', dx=5, fontSize=9).encode(
         x='population:Q', y='PM2\.5:Q', text='state:N'
     )
     return (points + labels).properties(title='PM2.5 vs Population by State – 2023', width=500, height=400)


</code>
"
412,temporal_aggregation,Plot the weekly average PM2.5 for Ratlam in 2020 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Ratlam') & (data['Timestamp'].dt.year == 2020)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Ratlam 2020', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Ratlam') & (data['Timestamp'].dt.year == 2020)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Ratlam 2020', width=600, height=300)
     return chart


</code>
"
413,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Jammu and Kashmir, Telangana, and Jammu and Kashmir from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Jammu and Kashmir', 'Telangana', 'Jammu and Kashmir']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Jammu and Kashmir vs Telangana vs Jammu and Kashmir', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Jammu and Kashmir', 'Telangana', 'Jammu and Kashmir']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Jammu and Kashmir vs Telangana vs Jammu and Kashmir', width=550, height=320)
     return chart


</code>
"
414,spatio_temporal_aggregation,Create a heatmap showing the average PM2.5 by month (x-axis) and year (y-axis) for Puducherry.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Puducherry'].copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_rect().encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Year:N', title='Year'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='Avg PM2.5'),
        tooltip=['Year:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Heatmap – Puducherry (Month × Year)', width=500, height=280)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Puducherry'].copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_rect().encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Year:N', title='Year'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='Avg PM2.5'),
         tooltip=['Year:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Heatmap – Puducherry (Month × Year)', width=500, height=280)
     return chart


</code>
"
415,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Bathinda, Satna, and Darbhanga in 2019 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Bathinda', 'Satna', 'Darbhanga']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Bathinda vs Satna vs Darbhanga – 2019', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Bathinda', 'Satna', 'Darbhanga']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Bathinda vs Satna vs Darbhanga – 2019', width=550, height=320)
     return chart


</code>
"
416,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Rajasthan, Tamil Nadu, and Jharkhand across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Rajasthan', 'Tamil Nadu', 'Jharkhand']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Rajasthan', 'Tamil Nadu', 'Jharkhand']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
417,temporal_aggregation,Show the monthly average PM10 trend for Vrindavan from 2017 to 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Vrindavan'].copy()
    df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Vrindavan (2017–2022)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Vrindavan'].copy()
     df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Vrindavan (2017–2022)', width=600, height=300)
     return chart


</code>
"
418,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Tripura stations in 2019, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Tripura') & (data['Timestamp'].dt.year == 2019)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Tripura Stations 2019', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Tripura') & (data['Timestamp'].dt.year == 2019)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Tripura Stations 2019', width=450, height=350)


</code>
"
419,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for West Bengal, Nagaland, and Himachal Pradesh across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['West Bengal', 'Nagaland', 'Himachal Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['West Bengal', 'Nagaland', 'Himachal Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
420,spatio_temporal_aggregation,"Create a faceted bar chart showing top 6 states by average PM2.5 per year for 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year.isin([2020,2021,2022,2023])].copy()
    df['Year'] = df['Timestamp'].dt.year
    agg = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    top = agg.groupby('Year').apply(lambda x: x.nlargest(6,'PM2.5')).reset_index(drop=True)
    chart = alt.Chart(top).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Avg PM2.5'),
        y=alt.Y('state:N', sort='-x'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).facet(facet='Year:O', columns=2).properties(title='Top 6 States by PM2.5 per Year')
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year.isin([2020,2021,2022,2023])].copy()
     df['Year'] = df['Timestamp'].dt.year
     agg = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     top = agg.groupby('Year').apply(lambda x: x.nlargest(6,'PM2.5')).reset_index(drop=True)
     chart = alt.Chart(top).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Avg PM2.5'),
         y=alt.Y('state:N', sort='-x'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).facet(facet='Year:O', columns=2).properties(title='Top 6 States by PM2.5 per Year')
     return chart


</code>
"
421,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Tripura, Jharkhand, and Bihar from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Tripura', 'Jharkhand', 'Bihar']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Tripura vs Jharkhand vs Bihar', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Tripura', 'Jharkhand', 'Bihar']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Tripura vs Jharkhand vs Bihar', width=550, height=320)
     return chart


</code>
"
422,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Tamil Nadu, Tripura, and Kerala from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Tamil Nadu', 'Tripura', 'Kerala']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Tamil Nadu vs Tripura vs Kerala', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Tamil Nadu', 'Tripura', 'Kerala']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Tamil Nadu vs Tripura vs Kerala', width=550, height=320)
     return chart


</code>
"
423,specific_pattern,Plot the rolling 30-day average PM2.5 for Meghalaya in 2023 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Meghalaya') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Meghalaya 2023', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Meghalaya') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Meghalaya 2023', width=600, height=300)


</code>
"
424,spatial_aggregation,"Show the top 15 states by average PM10 in 2022 as a bar chart, sorted in descending order.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2022]
    df = df.groupby('state')['PM10'].mean().reset_index().dropna()
    df = df.nlargest(15, 'PM10')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Top 15 States by Average PM10 in 2022', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2022]
     df = df.groupby('state')['PM10'].mean().reset_index().dropna()
     df = df.nlargest(15, 'PM10')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Top 15 States by Average PM10 in 2022', width=500, height=300)
     return chart


</code>
"
425,specific_pattern,Show a cumulative area chart of PM2.5 readings for Ankleshwar across 2019.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Ankleshwar') & (data['Timestamp'].dt.year == 2019)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Ankleshwar 2019', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Ankleshwar') & (data['Timestamp'].dt.year == 2019)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Ankleshwar 2019', width=600, height=300)
     return chart


</code>
"
426,spatio_temporal_aggregation,Plot a heatmap of average PM10 by state (y-axis) and month (x-axis) for 2019.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2019].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['state','Month'])['PM10'].mean().reset_index().dropna()
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_rect().encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('state:N', title='State'),
        color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), title='Avg PM10'),
        tooltip=['state:N','MonthName:N', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='PM10 Heatmap by State and Month – 2019', width=500, height=400)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2019].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['state','Month'])['PM10'].mean().reset_index().dropna()
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_rect().encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('state:N', title='State'),
         color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), title='Avg PM10'),
         tooltip=['state:N','MonthName:N', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='PM10 Heatmap by State and Month – 2019', width=500, height=400)
     return chart


</code>
"
427,spatial_aggregation,Plot the top 7 states by average PM2.5 in 2018 as a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2018]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nlargest(7, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 7 States by Average PM2.5 in 2018', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2018]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nlargest(7, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 7 States by Average PM2.5 in 2018', width=500, height=300)
     return chart


</code>
"
428,area_based,Show the PM2.5 per 1000 km² (air quality density) for each state in 2023 as a bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    pm = data[data['Timestamp'].dt.year == 2023].groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = pm.merge(states_data[['state','area (km2)']], on='state')
    df['PM2.5 per 1000 km²'] = df['PM2.5'] / df['area (km2)'] * 1000
    df = df.sort_values('PM2.5 per 1000 km²', ascending=False)
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5 per 1000 km²:Q', title='PM2.5 per 1000 km²'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM2\.5 per 1000 km²:Q', scale=alt.Scale(scheme='orangered'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5 per 1000 km²:Q', format='.3f')]
    ).properties(title='Air Quality Density (PM2.5 per 1000 km²) by State – 2023', width=500, height=400)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     pm = data[data['Timestamp'].dt.year == 2023].groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = pm.merge(states_data[['state','area (km2)']], on='state')
     df['PM2.5 per 1000 km²'] = df['PM2.5'] / df['area (km2)'] * 1000
     df = df.sort_values('PM2.5 per 1000 km²', ascending=False)
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5 per 1000 km²:Q', title='PM2.5 per 1000 km²'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM2\.5 per 1000 km²:Q', scale=alt.Scale(scheme='orangered'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5 per 1000 km²:Q', format='.3f')]
     ).properties(title='Air Quality Density (PM2.5 per 1000 km²) by State – 2023', width=500, height=400)
     return chart


</code>
"
429,spatial_aggregation,Plot the distribution of PM2.5 values in Andhra Pradesh across all years using a histogram.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Andhra Pradesh'].dropna(subset=['PM2.5'])
    df = df[['PM2.5']]
    chart = alt.Chart(df).mark_bar(color='steelblue', opacity=0.75).encode(
        x=alt.X('PM2\.5:Q', bin=alt.Bin(maxbins=40), title='PM2.5 (µg/m³)'),
        y=alt.Y('count()', title='Number of Observations'),
        tooltip=[alt.Tooltip('PM2\.5:Q', bin=True), 'count()']
    ).properties(title='PM2.5 Distribution – Andhra Pradesh (All Years)', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Andhra Pradesh'].dropna(subset=['PM2.5'])
     df = df[['PM2.5']]
     chart = alt.Chart(df).mark_bar(color='steelblue', opacity=0.75).encode(
         x=alt.X('PM2\.5:Q', bin=alt.Bin(maxbins=40), title='PM2.5 (µg/m³)'),
         y=alt.Y('count()', title='Number of Observations'),
         tooltip=[alt.Tooltip('PM2\.5:Q', bin=True), 'count()']
     ).properties(title='PM2.5 Distribution – Andhra Pradesh (All Years)', width=500, height=300)
     return chart


</code>
"
430,spatio_temporal_aggregation,Create a heatmap showing the average PM2.5 by month (x-axis) and year (y-axis) for Sikkim.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Sikkim'].copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_rect().encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Year:N', title='Year'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='Avg PM2.5'),
        tooltip=['Year:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Heatmap – Sikkim (Month × Year)', width=500, height=280)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Sikkim'].copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_rect().encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Year:N', title='Year'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='Avg PM2.5'),
         tooltip=['Year:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Heatmap – Sikkim (Month × Year)', width=500, height=280)
     return chart


</code>
"
431,temporal_aggregation,Show a monthly bar chart of the number of days Nagaland exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2024.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Nagaland') & (data['Timestamp'].dt.year == 2024)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Nagaland Exceeded WHO PM2.5 Guideline per Month – 2024', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Nagaland') & (data['Timestamp'].dt.year == 2024)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Nagaland Exceeded WHO PM2.5 Guideline per Month – 2024', width=500, height=300)
     return chart


</code>
"
432,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Himachal Pradesh, Mizoram, and Puducherry across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Himachal Pradesh', 'Mizoram', 'Puducherry']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Himachal Pradesh', 'Mizoram', 'Puducherry']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
433,specific_pattern,Show PM2.5 exceedances above the Indian standard (60 µg/m³) per year as a bar chart for Himachal Pradesh.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Himachal Pradesh'].dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 60].copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby('Year')['Timestamp'].nunique().reset_index()
    df.columns = ['Year','Days Exceeded']
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('Days Exceeded:Q', title='Days PM2.5 > 60 µg/m³'),
        tooltip=['Year:N','Days Exceeded:Q']
    ).properties(title='Days Himachal Pradesh Exceeded Indian PM2.5 Standard (60 µg/m³) per Year', width=450, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Himachal Pradesh'].dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 60].copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby('Year')['Timestamp'].nunique().reset_index()
     df.columns = ['Year','Days Exceeded']
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('Days Exceeded:Q', title='Days PM2.5 > 60 µg/m³'),
         tooltip=['Year:N','Days Exceeded:Q']
     ).properties(title='Days Himachal Pradesh Exceeded Indian PM2.5 Standard (60 µg/m³) per Year', width=450, height=300)
     return chart


</code>
"
434,spatial_aggregation,Visualize the bottom 11 states with the lowest average PM2.5 in 2018 using a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2018]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nsmallest(11, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Bottom 11 States by Average PM2.5 in 2018', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2018]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nsmallest(11, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Bottom 11 States by Average PM2.5 in 2018', width=500, height=300)
     return chart


</code>
"
435,spatial_aggregation,Plot the top 9 states by average PM2.5 in 2018 as a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2018]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nlargest(9, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 9 States by Average PM2.5 in 2018', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2018]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nlargest(9, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 9 States by Average PM2.5 in 2018', width=500, height=300)
     return chart


</code>
"
436,temporal_aggregation,Plot the weekly average PM2.5 for Chikkamagaluru in 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Chikkamagaluru') & (data['Timestamp'].dt.year == 2024)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Chikkamagaluru 2024', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Chikkamagaluru') & (data['Timestamp'].dt.year == 2024)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Chikkamagaluru 2024', width=600, height=300)
     return chart


</code>
"
437,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Ahmednagar, Chamarajanagar, and Ludhiana in 2019 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Ahmednagar', 'Chamarajanagar', 'Ludhiana']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Ahmednagar vs Chamarajanagar vs Ludhiana – 2019', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Ahmednagar', 'Chamarajanagar', 'Ludhiana']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Ahmednagar vs Chamarajanagar vs Ludhiana – 2019', width=550, height=320)
     return chart


</code>
"
438,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Chandigarh, Meghalaya, and Karnataka across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Chandigarh', 'Meghalaya', 'Karnataka']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Chandigarh', 'Meghalaya', 'Karnataka']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
439,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Manipur, Telangana, and Meghalaya from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Manipur', 'Telangana', 'Meghalaya']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Manipur vs Telangana vs Meghalaya', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Manipur', 'Telangana', 'Meghalaya']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Manipur vs Telangana vs Meghalaya', width=550, height=320)
     return chart


</code>
"
440,spatial_aggregation,Show a bar chart of the top 5 cities by median PM2.5 in 2020.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2020]
    df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
    df = df.nlargest(5, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
        y=alt.Y('city:N', sort='-x', title='City'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
        tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 5 Cities by Median PM2.5 in 2020', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2020]
     df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
     df = df.nlargest(5, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
         y=alt.Y('city:N', sort='-x', title='City'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
         tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 5 Cities by Median PM2.5 in 2020', width=500, height=300)
     return chart


</code>
"
441,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Madhya Pradesh, Assam, and Uttarakhand across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Madhya Pradesh', 'Assam', 'Uttarakhand']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Madhya Pradesh', 'Assam', 'Uttarakhand']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
442,spatio_temporal_aggregation,Create a heatmap showing the average PM2.5 by month (x-axis) and year (y-axis) for Mizoram.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Mizoram'].copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_rect().encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Year:N', title='Year'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='Avg PM2.5'),
        tooltip=['Year:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Heatmap – Mizoram (Month × Year)', width=500, height=280)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Mizoram'].copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_rect().encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Year:N', title='Year'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='Avg PM2.5'),
         tooltip=['Year:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Heatmap – Mizoram (Month × Year)', width=500, height=280)
     return chart


</code>
"
443,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Punjab, Kerala, and Odisha across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Punjab', 'Kerala', 'Odisha']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Punjab', 'Kerala', 'Odisha']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
444,spatio_temporal_aggregation,Create a heatmap showing the average PM2.5 by month (x-axis) and year (y-axis) for Chhattisgarh.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Chhattisgarh'].copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_rect().encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Year:N', title='Year'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='Avg PM2.5'),
        tooltip=['Year:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Heatmap – Chhattisgarh (Month × Year)', width=500, height=280)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Chhattisgarh'].copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_rect().encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Year:N', title='Year'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='Avg PM2.5'),
         tooltip=['Year:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Heatmap – Chhattisgarh (Month × Year)', width=500, height=280)
     return chart


</code>
"
445,spatial_aggregation,Show a box plot of PM2.5 distribution for each state in 2021.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2021].dropna(subset=['PM2.5'])
    df = df[['state','PM2.5']]
    state_order = df.groupby('state')['PM2.5'].median().sort_values(ascending=False).index.tolist()
    chart = alt.Chart(df).mark_boxplot(extent='min-max').encode(
        x=alt.X('PM2\.5:Q', title='PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort=state_order, title='State'),
        color=alt.Color('state:N', legend=None)
    ).properties(title='PM2.5 Distribution by State – 2021', width=500, height=450)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2021].dropna(subset=['PM2.5'])
     df = df[['state','PM2.5']]
     state_order = df.groupby('state')['PM2.5'].median().sort_values(ascending=False).index.tolist()
     chart = alt.Chart(df).mark_boxplot(extent='min-max').encode(
         x=alt.X('PM2\.5:Q', title='PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort=state_order, title='State'),
         color=alt.Color('state:N', legend=None)
     ).properties(title='PM2.5 Distribution by State – 2021', width=500, height=450)
     return chart


</code>
"
446,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Gujarat, Mizoram, and Kerala across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Gujarat', 'Mizoram', 'Kerala']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Gujarat', 'Mizoram', 'Kerala']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
447,population_based,"Plot a scatter chart of state-level average PM2.5 versus population for 2022, with point size representing area.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    pm = data[data['Timestamp'].dt.year == 2022].groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = pm.merge(states_data, on='state')
    chart = alt.Chart(df).mark_point(filled=True, opacity=0.7).encode(
        x=alt.X('population:Q', title='Population', axis=alt.Axis(format='~s')),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        size=alt.Size('area (km2):Q', title='Area (km²)', scale=alt.Scale(range=[50,1000])),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f'),
                 alt.Tooltip('population:Q', format=','), 'area (km2):Q']
    ).properties(title='PM2.5 vs Population (size=Area) – 2022', width=500, height=400)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     pm = data[data['Timestamp'].dt.year == 2022].groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = pm.merge(states_data, on='state')
     chart = alt.Chart(df).mark_point(filled=True, opacity=0.7).encode(
         x=alt.X('population:Q', title='Population', axis=alt.Axis(format='~s')),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         size=alt.Size('area (km2):Q', title='Area (km²)', scale=alt.Scale(range=[50,1000])),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f'),
                  alt.Tooltip('population:Q', format=','), 'area (km2):Q']
     ).properties(title='PM2.5 vs Population (size=Area) – 2022', width=500, height=400)
     return chart


</code>
"
448,spatio_temporal_aggregation,"Create a faceted bar chart showing top 8 states by average PM2.5 per year for 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year.isin([2020,2021,2022,2023])].copy()
    df['Year'] = df['Timestamp'].dt.year
    agg = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    top = agg.groupby('Year').apply(lambda x: x.nlargest(8,'PM2.5')).reset_index(drop=True)
    chart = alt.Chart(top).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Avg PM2.5'),
        y=alt.Y('state:N', sort='-x'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).facet(facet='Year:O', columns=2).properties(title='Top 8 States by PM2.5 per Year')
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year.isin([2020,2021,2022,2023])].copy()
     df['Year'] = df['Timestamp'].dt.year
     agg = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     top = agg.groupby('Year').apply(lambda x: x.nlargest(8,'PM2.5')).reset_index(drop=True)
     chart = alt.Chart(top).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Avg PM2.5'),
         y=alt.Y('state:N', sort='-x'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).facet(facet='Year:O', columns=2).properties(title='Top 8 States by PM2.5 per Year')
     return chart


</code>
"
449,temporal_aggregation,Show the monthly average PM2.5 for Manguraha in 2024 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Manguraha') & (data['Timestamp'].dt.year == 2024)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Manguraha 2024', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Manguraha') & (data['Timestamp'].dt.year == 2024)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Manguraha 2024', width=450, height=280)


</code>
"
450,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Haryana, Chandigarh, and Delhi in 2017 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Haryana', 'Chandigarh', 'Delhi']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2017)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Haryana, Chandigarh, UP – 2017', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Haryana', 'Chandigarh', 'Delhi']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2017)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Haryana, Chandigarh, UP – 2017', width=550, height=320)
     return chart


</code>
"
451,specific_pattern,Plot the rolling 30-day average PM2.5 for Odisha in 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Odisha') & (data['Timestamp'].dt.year == 2022)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Odisha 2022', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Odisha') & (data['Timestamp'].dt.year == 2022)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Odisha 2022', width=600, height=300)


</code>
"
452,spatial_aggregation,Plot the distribution of PM2.5 values in Jammu and Kashmir across all years using a histogram.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Jammu and Kashmir'].dropna(subset=['PM2.5'])
    df = df[['PM2.5']]
    chart = alt.Chart(df).mark_bar(color='steelblue', opacity=0.75).encode(
        x=alt.X('PM2\.5:Q', bin=alt.Bin(maxbins=40), title='PM2.5 (µg/m³)'),
        y=alt.Y('count()', title='Number of Observations'),
        tooltip=[alt.Tooltip('PM2\.5:Q', bin=True), 'count()']
    ).properties(title='PM2.5 Distribution – Jammu and Kashmir (All Years)', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Jammu and Kashmir'].dropna(subset=['PM2.5'])
     df = df[['PM2.5']]
     chart = alt.Chart(df).mark_bar(color='steelblue', opacity=0.75).encode(
         x=alt.X('PM2\.5:Q', bin=alt.Bin(maxbins=40), title='PM2.5 (µg/m³)'),
         y=alt.Y('count()', title='Number of Observations'),
         tooltip=[alt.Tooltip('PM2\.5:Q', bin=True), 'count()']
     ).properties(title='PM2.5 Distribution – Jammu and Kashmir (All Years)', width=500, height=300)
     return chart


</code>
"
453,spatio_temporal_aggregation,"Create a faceted bar chart showing top 12 states by average PM2.5 per year for 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year.isin([2021,2022,2023,2024])].copy()
    df['Year'] = df['Timestamp'].dt.year
    agg = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    top = agg.groupby('Year').apply(lambda x: x.nlargest(12,'PM2.5')).reset_index(drop=True)
    chart = alt.Chart(top).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Avg PM2.5'),
        y=alt.Y('state:N', sort='-x'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).facet(facet='Year:O', columns=2).properties(title='Top 12 States by PM2.5 per Year')
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year.isin([2021,2022,2023,2024])].copy()
     df['Year'] = df['Timestamp'].dt.year
     agg = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     top = agg.groupby('Year').apply(lambda x: x.nlargest(12,'PM2.5')).reset_index(drop=True)
     chart = alt.Chart(top).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Avg PM2.5'),
         y=alt.Y('state:N', sort='-x'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).facet(facet='Year:O', columns=2).properties(title='Top 12 States by PM2.5 per Year')
     return chart


</code>
"
454,spatial_aggregation,Visualize the bottom 10 states with the lowest average PM2.5 in 2017 using a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2017]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nsmallest(10, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Bottom 10 States by Average PM2.5 in 2017', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2017]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nsmallest(10, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Bottom 10 States by Average PM2.5 in 2017', width=500, height=300)
     return chart


</code>
"
455,spatio_temporal_aggregation,Show the monthly average PM2.5 for Assam across each year from 2017 to 2024 as small multiples (faceted by year).,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Assam'].copy()
    df['Year'] = df['Timestamp'].dt.year
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='steelblue').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5'),
        tooltip=['Year:O','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).facet(
        facet=alt.Facet('Year:O', title='Year'),
        columns=4
    ).properties(title='Monthly PM2.5 in Assam by Year (2017–2024)')
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Assam'].copy()
     df['Year'] = df['Timestamp'].dt.year
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='steelblue').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5'),
         tooltip=['Year:O','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).facet(
         facet=alt.Facet('Year:O', title='Year'),
         columns=4
     ).properties(title='Monthly PM2.5 in Assam by Year (2017–2024)')
     return chart


</code>
"
456,spatial_aggregation,Visualize the bottom 14 states with the lowest average PM2.5 in 2020 using a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2020]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nsmallest(14, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Bottom 14 States by Average PM2.5 in 2020', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2020]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nsmallest(14, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Bottom 14 States by Average PM2.5 in 2020', width=500, height=300)
     return chart


</code>
"
457,temporal_aggregation,Show the monthly average PM2.5 for Sawai Madhopur in 2019 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Sawai Madhopur') & (data['Timestamp'].dt.year == 2019)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Sawai Madhopur 2019', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Sawai Madhopur') & (data['Timestamp'].dt.year == 2019)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Sawai Madhopur 2019', width=450, height=280)


</code>
"
458,spatio_temporal_aggregation,Show a heatmap of average PM2.5 for the top 9 most polluted states by month for 2019.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    top8 = data.groupby('state')['PM2.5'].mean().nlargest(9).index.tolist()
    df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_rect().encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('state:N', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
        tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Heatmap – Top 9 Polluted States by Month (2019)', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     top8 = data.groupby('state')['PM2.5'].mean().nlargest(9).index.tolist()
     df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_rect().encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('state:N', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
         tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Heatmap – Top 9 Polluted States by Month (2019)', width=500, height=300)
     return chart


</code>
"
459,spatio_temporal_aggregation,Plot a heatmap of average PM10 by state (y-axis) and month (x-axis) for 2023.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2023].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['state','Month'])['PM10'].mean().reset_index().dropna()
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_rect().encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('state:N', title='State'),
        color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), title='Avg PM10'),
        tooltip=['state:N','MonthName:N', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='PM10 Heatmap by State and Month – 2023', width=500, height=400)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2023].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['state','Month'])['PM10'].mean().reset_index().dropna()
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_rect().encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('state:N', title='State'),
         color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), title='Avg PM10'),
         tooltip=['state:N','MonthName:N', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='PM10 Heatmap by State and Month – 2023', width=500, height=400)
     return chart


</code>
"
460,spatial_aggregation,Plot the distribution of PM2.5 values in Odisha across all years using a histogram.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Odisha'].dropna(subset=['PM2.5'])
    df = df[['PM2.5']]
    chart = alt.Chart(df).mark_bar(color='steelblue', opacity=0.75).encode(
        x=alt.X('PM2\.5:Q', bin=alt.Bin(maxbins=40), title='PM2.5 (µg/m³)'),
        y=alt.Y('count()', title='Number of Observations'),
        tooltip=[alt.Tooltip('PM2\.5:Q', bin=True), 'count()']
    ).properties(title='PM2.5 Distribution – Odisha (All Years)', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Odisha'].dropna(subset=['PM2.5'])
     df = df[['PM2.5']]
     chart = alt.Chart(df).mark_bar(color='steelblue', opacity=0.75).encode(
         x=alt.X('PM2\.5:Q', bin=alt.Bin(maxbins=40), title='PM2.5 (µg/m³)'),
         y=alt.Y('count()', title='Number of Observations'),
         tooltip=[alt.Tooltip('PM2\.5:Q', bin=True), 'count()']
     ).properties(title='PM2.5 Distribution – Odisha (All Years)', width=500, height=300)
     return chart


</code>
"
461,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Bharatpur, Mahad, and Cuttack in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Bharatpur', 'Mahad', 'Cuttack']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Bharatpur vs Mahad vs Cuttack – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Bharatpur', 'Mahad', 'Cuttack']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Bharatpur vs Mahad vs Cuttack – 2023', width=550, height=320)
     return chart


</code>
"
462,spatio_temporal_aggregation,"Create a faceted bar chart showing top 7 states by average PM2.5 per year for 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year.isin([2021,2022,2023,2024])].copy()
    df['Year'] = df['Timestamp'].dt.year
    agg = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    top = agg.groupby('Year').apply(lambda x: x.nlargest(7,'PM2.5')).reset_index(drop=True)
    chart = alt.Chart(top).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Avg PM2.5'),
        y=alt.Y('state:N', sort='-x'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).facet(facet='Year:O', columns=2).properties(title='Top 7 States by PM2.5 per Year')
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year.isin([2021,2022,2023,2024])].copy()
     df['Year'] = df['Timestamp'].dt.year
     agg = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     top = agg.groupby('Year').apply(lambda x: x.nlargest(7,'PM2.5')).reset_index(drop=True)
     chart = alt.Chart(top).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Avg PM2.5'),
         y=alt.Y('state:N', sort='-x'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).facet(facet='Year:O', columns=2).properties(title='Top 7 States by PM2.5 per Year')
     return chart


</code>
"
463,spatial_aggregation,"Show the top 9 states by average PM10 in 2020 as a bar chart, sorted in descending order.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2020]
    df = df.groupby('state')['PM10'].mean().reset_index().dropna()
    df = df.nlargest(9, 'PM10')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Top 9 States by Average PM10 in 2020', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2020]
     df = df.groupby('state')['PM10'].mean().reset_index().dropna()
     df = df.nlargest(9, 'PM10')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Top 9 States by Average PM10 in 2020', width=500, height=300)
     return chart


</code>
"
464,temporal_aggregation,Plot the weekly average PM2.5 for Siliguri in 2019 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Siliguri') & (data['Timestamp'].dt.year == 2019)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Siliguri 2019', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Siliguri') & (data['Timestamp'].dt.year == 2019)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Siliguri 2019', width=600, height=300)
     return chart


</code>
"
465,temporal_aggregation,Show the monthly average PM2.5 for Chikkamagaluru in 2018 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Chikkamagaluru') & (data['Timestamp'].dt.year == 2018)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Chikkamagaluru 2018', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Chikkamagaluru') & (data['Timestamp'].dt.year == 2018)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Chikkamagaluru 2018', width=450, height=280)


</code>
"
466,spatial_aggregation,Show a bar chart of the top 6 cities by median PM2.5 in 2019.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2019]
    df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
    df = df.nlargest(6, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
        y=alt.Y('city:N', sort='-x', title='City'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
        tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 6 Cities by Median PM2.5 in 2019', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2019]
     df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
     df = df.nlargest(6, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
         y=alt.Y('city:N', sort='-x', title='City'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
         tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 6 Cities by Median PM2.5 in 2019', width=500, height=300)
     return chart


</code>
"
467,spatial_aggregation,Visualize the bottom 10 states with the lowest average PM2.5 in 2023 using a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2023]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nsmallest(10, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Bottom 10 States by Average PM2.5 in 2023', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2023]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nsmallest(10, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Bottom 10 States by Average PM2.5 in 2023', width=500, height=300)
     return chart


</code>
"
468,temporal_aggregation,Show a monthly bar chart of the number of days Telangana exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2018.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Telangana') & (data['Timestamp'].dt.year == 2018)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Telangana Exceeded WHO PM2.5 Guideline per Month – 2018', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Telangana') & (data['Timestamp'].dt.year == 2018)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Telangana Exceeded WHO PM2.5 Guideline per Month – 2018', width=500, height=300)
     return chart


</code>
"
469,specific_pattern,Show a cumulative area chart of PM2.5 readings for Dhanbad across 2024.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Dhanbad') & (data['Timestamp'].dt.year == 2024)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Dhanbad 2024', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Dhanbad') & (data['Timestamp'].dt.year == 2024)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Dhanbad 2024', width=600, height=300)
     return chart


</code>
"
470,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Puducherry stations in 2019, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Puducherry') & (data['Timestamp'].dt.year == 2019)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Puducherry Stations 2019', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Puducherry') & (data['Timestamp'].dt.year == 2019)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Puducherry Stations 2019', width=450, height=350)


</code>
"
471,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Himachal Pradesh, Nagaland, and Sikkim in 2021 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Himachal Pradesh', 'Nagaland', 'Sikkim']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Himachal Pradesh, Nagaland, UP – 2021', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Himachal Pradesh', 'Nagaland', 'Sikkim']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Himachal Pradesh, Nagaland, UP – 2021', width=550, height=320)
     return chart


</code>
"
472,funding_based,Visualize NCAP city-level funding for FY 2021-22 as a horizontal bar chart for the top 9 cities.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = ncap_funding_data[['city','state','Amount released during FY 2021-22']].copy()
    df.columns = ['city','state','Amount (Cr)']
    df = df.nlargest(9, 'Amount (Cr)')
    df['City_State'] = df['city'] + ', ' + df['state']
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Amount (Cr):Q', title='Amount Released (Cr)'),
        y=alt.Y('City_State:N', sort='-x', title='City, State'),
        color=alt.Color('state:N', title='State'),
        tooltip=['city:N','state:N', alt.Tooltip('Amount (Cr):Q', format='.1f')]
    ).properties(title='Top 9 Cities by NCAP Funding – FY 2021-22', width=500, height=400)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = ncap_funding_data[['city','state','Amount released during FY 2021-22']].copy()
     df.columns = ['city','state','Amount (Cr)']
     df = df.nlargest(9, 'Amount (Cr)')
     df['City_State'] = df['city'] + ', ' + df['state']
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Amount (Cr):Q', title='Amount Released (Cr)'),
         y=alt.Y('City_State:N', sort='-x', title='City, State'),
         color=alt.Color('state:N', title='State'),
         tooltip=['city:N','state:N', alt.Tooltip('Amount (Cr):Q', format='.1f')]
     ).properties(title='Top 9 Cities by NCAP Funding – FY 2021-22', width=500, height=400)
     return chart


</code>
"
473,spatio_temporal_aggregation,Show a heatmap of average PM2.5 for the top 14 most polluted states by month for 2023.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    top8 = data.groupby('state')['PM2.5'].mean().nlargest(14).index.tolist()
    df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_rect().encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('state:N', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
        tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Heatmap – Top 14 Polluted States by Month (2023)', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     top8 = data.groupby('state')['PM2.5'].mean().nlargest(14).index.tolist()
     df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_rect().encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('state:N', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
         tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Heatmap – Top 14 Polluted States by Month (2023)', width=500, height=300)
     return chart


</code>
"
474,temporal_aggregation,Show a monthly bar chart of the number of days Delhi exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2024.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Delhi') & (data['Timestamp'].dt.year == 2024)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Delhi Exceeded WHO PM2.5 Guideline per Month – 2024', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Delhi') & (data['Timestamp'].dt.year == 2024)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Delhi Exceeded WHO PM2.5 Guideline per Month – 2024', width=500, height=300)
     return chart


</code>
"
475,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Vijayawada, Nagapattinam, and Sikar in 2017 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Vijayawada', 'Nagapattinam', 'Sikar']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2017)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Vijayawada vs Nagapattinam vs Sikar – 2017', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Vijayawada', 'Nagapattinam', 'Sikar']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2017)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Vijayawada vs Nagapattinam vs Sikar – 2017', width=550, height=320)
     return chart


</code>
"
476,funding_based,Show a scatter plot of total NCAP funding vs average PM2.5 (2024) per state.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    pm = data[data['Timestamp'].dt.year == 2024].groupby('state')['PM2.5'].mean().reset_index()
    funding = ncap_funding_data.groupby('state')['Total fund released'].sum().reset_index()
    df = pm.merge(funding, on='state').dropna()
    chart = alt.Chart(df).mark_point(filled=True, size=100).encode(
        x=alt.X('Total fund released:Q', title='Total NCAP Funding (Cr)'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 – 2024 (µg/m³)'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f'),
                 alt.Tooltip('Total fund released:Q', format='.1f')]
    ).properties(title='NCAP Funding vs Average PM2.5 by State (2024)', width=450, height=350)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     pm = data[data['Timestamp'].dt.year == 2024].groupby('state')['PM2.5'].mean().reset_index()
     funding = ncap_funding_data.groupby('state')['Total fund released'].sum().reset_index()
     df = pm.merge(funding, on='state').dropna()
     chart = alt.Chart(df).mark_point(filled=True, size=100).encode(
         x=alt.X('Total fund released:Q', title='Total NCAP Funding (Cr)'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 – 2024 (µg/m³)'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f'),
                  alt.Tooltip('Total fund released:Q', format='.1f')]
     ).properties(title='NCAP Funding vs Average PM2.5 by State (2024)', width=450, height=350)
     return chart


</code>
"
477,temporal_aggregation,Plot the monthly average PM2.5 trend for Himachal Pradesh from 2017 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Himachal Pradesh'].copy()
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='steelblue').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly Average PM2.5 Trend – Himachal Pradesh (2017–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Himachal Pradesh'].copy()
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='steelblue').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly Average PM2.5 Trend – Himachal Pradesh (2017–2024)', width=600, height=300)
     return chart


</code>
"
478,temporal_aggregation,Show a monthly bar chart of the number of days Madhya Pradesh exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2020.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Madhya Pradesh') & (data['Timestamp'].dt.year == 2020)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Madhya Pradesh Exceeded WHO PM2.5 Guideline per Month – 2020', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Madhya Pradesh') & (data['Timestamp'].dt.year == 2020)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Madhya Pradesh Exceeded WHO PM2.5 Guideline per Month – 2020', width=500, height=300)
     return chart


</code>
"
479,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for West Bengal, Gujarat, and Uttarakhand across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['West Bengal', 'Gujarat', 'Uttarakhand']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['West Bengal', 'Gujarat', 'Uttarakhand']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
480,spatial_aggregation,"Show the top 5 states by average PM10 in 2021 as a bar chart, sorted in descending order.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2021]
    df = df.groupby('state')['PM10'].mean().reset_index().dropna()
    df = df.nlargest(5, 'PM10')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Top 5 States by Average PM10 in 2021', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2021]
     df = df.groupby('state')['PM10'].mean().reset_index().dropna()
     df = df.nlargest(5, 'PM10')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Top 5 States by Average PM10 in 2021', width=500, height=300)
     return chart


</code>
"
481,spatio_temporal_aggregation,Show a heatmap of average PM2.5 for the top 11 most polluted states by month for 2018.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    top8 = data.groupby('state')['PM2.5'].mean().nlargest(11).index.tolist()
    df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2018)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_rect().encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('state:N', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
        tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Heatmap – Top 11 Polluted States by Month (2018)', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     top8 = data.groupby('state')['PM2.5'].mean().nlargest(11).index.tolist()
     df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2018)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_rect().encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('state:N', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
         tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Heatmap – Top 11 Polluted States by Month (2018)', width=500, height=300)
     return chart


</code>
"
482,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Andhra Pradesh, Uttarakhand, and Jammu and Kashmir from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Andhra Pradesh', 'Uttarakhand', 'Jammu and Kashmir']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Andhra Pradesh vs Uttarakhand vs Jammu and Kashmir', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Andhra Pradesh', 'Uttarakhand', 'Jammu and Kashmir']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Andhra Pradesh vs Uttarakhand vs Jammu and Kashmir', width=550, height=320)
     return chart


</code>
"
483,temporal_aggregation,Show the monthly average PM10 trend for Katihar from 2017 to 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Katihar'].copy()
    df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Katihar (2017–2022)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Katihar'].copy()
     df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Katihar (2017–2022)', width=600, height=300)
     return chart


</code>
"
484,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Sikkim, Gujarat, and Himachal Pradesh across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Sikkim', 'Gujarat', 'Himachal Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Sikkim', 'Gujarat', 'Himachal Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
485,spatio_temporal_aggregation,"Create a faceted bar chart showing top 5 states by average PM2.5 per year for 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year.isin([2017,2018,2019,2020])].copy()
    df['Year'] = df['Timestamp'].dt.year
    agg = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    top = agg.groupby('Year').apply(lambda x: x.nlargest(5,'PM2.5')).reset_index(drop=True)
    chart = alt.Chart(top).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Avg PM2.5'),
        y=alt.Y('state:N', sort='-x'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).facet(facet='Year:O', columns=2).properties(title='Top 5 States by PM2.5 per Year')
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year.isin([2017,2018,2019,2020])].copy()
     df['Year'] = df['Timestamp'].dt.year
     agg = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     top = agg.groupby('Year').apply(lambda x: x.nlargest(5,'PM2.5')).reset_index(drop=True)
     chart = alt.Chart(top).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Avg PM2.5'),
         y=alt.Y('state:N', sort='-x'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).facet(facet='Year:O', columns=2).properties(title='Top 5 States by PM2.5 per Year')
     return chart


</code>
"
486,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Tamil Nadu, Odisha, and Nagaland in 2021 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Tamil Nadu', 'Odisha', 'Nagaland']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Tamil Nadu, Odisha, UP – 2021', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Tamil Nadu', 'Odisha', 'Nagaland']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Tamil Nadu, Odisha, UP – 2021', width=550, height=320)
     return chart


</code>
"
487,temporal_aggregation,Plot the monthly average PM2.5 trend for Telangana from 2017 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Telangana'].copy()
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='steelblue').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly Average PM2.5 Trend – Telangana (2017–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Telangana'].copy()
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='steelblue').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly Average PM2.5 Trend – Telangana (2017–2024)', width=600, height=300)
     return chart


</code>
"
488,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Himachal Pradesh, Chandigarh, and Kerala in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Himachal Pradesh', 'Chandigarh', 'Kerala']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Himachal Pradesh, Chandigarh, UP – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Himachal Pradesh', 'Chandigarh', 'Kerala']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Himachal Pradesh, Chandigarh, UP – 2023', width=550, height=320)
     return chart


</code>
"
489,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Chhattisgarh, Kerala, and Puducherry in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Chhattisgarh', 'Kerala', 'Puducherry']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Chhattisgarh, Kerala, UP – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Chhattisgarh', 'Kerala', 'Puducherry']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Chhattisgarh, Kerala, UP – 2022', width=550, height=320)
     return chart


</code>
"
490,specific_pattern,Show PM2.5 exceedances above the Indian standard (60 µg/m³) per year as a bar chart for Assam.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Assam'].dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 60].copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby('Year')['Timestamp'].nunique().reset_index()
    df.columns = ['Year','Days Exceeded']
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('Days Exceeded:Q', title='Days PM2.5 > 60 µg/m³'),
        tooltip=['Year:N','Days Exceeded:Q']
    ).properties(title='Days Assam Exceeded Indian PM2.5 Standard (60 µg/m³) per Year', width=450, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Assam'].dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 60].copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby('Year')['Timestamp'].nunique().reset_index()
     df.columns = ['Year','Days Exceeded']
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('Days Exceeded:Q', title='Days PM2.5 > 60 µg/m³'),
         tooltip=['Year:N','Days Exceeded:Q']
     ).properties(title='Days Assam Exceeded Indian PM2.5 Standard (60 µg/m³) per Year', width=450, height=300)
     return chart


</code>
"
491,spatial_aggregation,Show a bar chart of the top 13 cities by median PM2.5 in 2021.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2021]
    df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
    df = df.nlargest(13, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
        y=alt.Y('city:N', sort='-x', title='City'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
        tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 13 Cities by Median PM2.5 in 2021', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2021]
     df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
     df = df.nlargest(13, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
         y=alt.Y('city:N', sort='-x', title='City'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
         tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 13 Cities by Median PM2.5 in 2021', width=500, height=300)
     return chart


</code>
"
492,spatial_aggregation,"Show the top 12 states by average PM10 in 2021 as a bar chart, sorted in descending order.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2021]
    df = df.groupby('state')['PM10'].mean().reset_index().dropna()
    df = df.nlargest(12, 'PM10')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Top 12 States by Average PM10 in 2021', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2021]
     df = df.groupby('state')['PM10'].mean().reset_index().dropna()
     df = df.nlargest(12, 'PM10')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Top 12 States by Average PM10 in 2021', width=500, height=300)
     return chart


</code>
"
493,spatial_aggregation,Visualize the bottom 11 states with the lowest average PM2.5 in 2023 using a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2023]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nsmallest(11, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Bottom 11 States by Average PM2.5 in 2023', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2023]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nsmallest(11, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Bottom 11 States by Average PM2.5 in 2023', width=500, height=300)
     return chart


</code>
"
494,spatio_temporal_aggregation,Show a heatmap of average PM2.5 for the top 5 most polluted states by month for 2019.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    top8 = data.groupby('state')['PM2.5'].mean().nlargest(5).index.tolist()
    df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_rect().encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('state:N', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
        tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Heatmap – Top 5 Polluted States by Month (2019)', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     top8 = data.groupby('state')['PM2.5'].mean().nlargest(5).index.tolist()
     df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_rect().encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('state:N', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
         tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Heatmap – Top 5 Polluted States by Month (2019)', width=500, height=300)
     return chart


</code>
"
495,spatial_aggregation,Show a bar chart of the top 8 cities by median PM2.5 in 2019.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2019]
    df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
    df = df.nlargest(8, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
        y=alt.Y('city:N', sort='-x', title='City'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
        tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 8 Cities by Median PM2.5 in 2019', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2019]
     df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
     df = df.nlargest(8, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
         y=alt.Y('city:N', sort='-x', title='City'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
         tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 8 Cities by Median PM2.5 in 2019', width=500, height=300)
     return chart


</code>
"
496,spatial_aggregation,Show a bar chart comparing the 75th percentile PM2.5 of all states in winter 2022 (November–February).,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.month.isin([11,12,1,2])]
    df = df[df['Timestamp'].dt.year.isin([2022,2023])]
    df = df.groupby('state')['PM2.5'].quantile(0.75).reset_index().dropna()
    df.columns = ['state','PM2.5_p75']
    df = df.sort_values('PM2.5_p75', ascending=False)
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5_p75:Q', title='75th Percentile PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM2\.5_p75:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5_p75:Q', format='.1f', title='P75 PM2.5')]
    ).properties(title='75th Percentile PM2.5 by State – Winter 2022', width=500, height=400)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.month.isin([11,12,1,2])]
     df = df[df['Timestamp'].dt.year.isin([2022,2023])]
     df = df.groupby('state')['PM2.5'].quantile(0.75).reset_index().dropna()
     df.columns = ['state','PM2.5_p75']
     df = df.sort_values('PM2.5_p75', ascending=False)
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5_p75:Q', title='75th Percentile PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM2\.5_p75:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5_p75:Q', format='.1f', title='P75 PM2.5')]
     ).properties(title='75th Percentile PM2.5 by State – Winter 2022', width=500, height=400)
     return chart


</code>
"
497,specific_pattern,Plot the rolling 30-day average PM2.5 for Punjab in 2020 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Punjab') & (data['Timestamp'].dt.year == 2020)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Punjab 2020', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Punjab') & (data['Timestamp'].dt.year == 2020)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Punjab 2020', width=600, height=300)


</code>
"
498,temporal_aggregation,Show the monthly average PM10 trend for Rourkela from 2017 to 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Rourkela'].copy()
    df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Rourkela (2017–2022)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Rourkela'].copy()
     df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Rourkela (2017–2022)', width=600, height=300)
     return chart


</code>
"
499,spatial_aggregation,Visualize the bottom 8 states with the lowest average PM2.5 in 2024 using a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2024]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nsmallest(8, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Bottom 8 States by Average PM2.5 in 2024', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2024]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nsmallest(8, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Bottom 8 States by Average PM2.5 in 2024', width=500, height=300)
     return chart


</code>
"
500,spatial_aggregation,Visualize the bottom 9 states with the lowest average PM2.5 in 2019 using a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2019]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nsmallest(9, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Bottom 9 States by Average PM2.5 in 2019', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2019]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nsmallest(9, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Bottom 9 States by Average PM2.5 in 2019', width=500, height=300)
     return chart


</code>
"
501,spatial_aggregation,Visualize the bottom 15 states with the lowest average PM2.5 in 2024 using a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2024]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nsmallest(15, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Bottom 15 States by Average PM2.5 in 2024', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2024]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nsmallest(15, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Bottom 15 States by Average PM2.5 in 2024', width=500, height=300)
     return chart


</code>
"
502,temporal_aggregation,Show the monthly average PM2.5 for Moradabad in 2019 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Moradabad') & (data['Timestamp'].dt.year == 2019)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Moradabad 2019', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Moradabad') & (data['Timestamp'].dt.year == 2019)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Moradabad 2019', width=450, height=280)


</code>
"
503,temporal_aggregation,Plot the monthly average PM2.5 trend for Tamil Nadu from 2017 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Tamil Nadu'].copy()
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='steelblue').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly Average PM2.5 Trend – Tamil Nadu (2017–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Tamil Nadu'].copy()
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='steelblue').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly Average PM2.5 Trend – Tamil Nadu (2017–2024)', width=600, height=300)
     return chart


</code>
"
504,spatio_temporal_aggregation,Show a heatmap of average PM2.5 for the top 11 most polluted states by month for 2021.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    top8 = data.groupby('state')['PM2.5'].mean().nlargest(11).index.tolist()
    df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2021)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_rect().encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('state:N', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
        tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Heatmap – Top 11 Polluted States by Month (2021)', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     top8 = data.groupby('state')['PM2.5'].mean().nlargest(11).index.tolist()
     df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2021)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_rect().encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('state:N', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
         tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Heatmap – Top 11 Polluted States by Month (2021)', width=500, height=300)
     return chart


</code>
"
505,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Kerala, Telangana, and Nagaland across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Kerala', 'Telangana', 'Nagaland']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Kerala', 'Telangana', 'Nagaland']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
506,funding_based,Visualize NCAP city-level funding for FY 2021-22 as a horizontal bar chart for the top 10 cities.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = ncap_funding_data[['city','state','Amount released during FY 2021-22']].copy()
    df.columns = ['city','state','Amount (Cr)']
    df = df.nlargest(10, 'Amount (Cr)')
    df['City_State'] = df['city'] + ', ' + df['state']
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Amount (Cr):Q', title='Amount Released (Cr)'),
        y=alt.Y('City_State:N', sort='-x', title='City, State'),
        color=alt.Color('state:N', title='State'),
        tooltip=['city:N','state:N', alt.Tooltip('Amount (Cr):Q', format='.1f')]
    ).properties(title='Top 10 Cities by NCAP Funding – FY 2021-22', width=500, height=400)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = ncap_funding_data[['city','state','Amount released during FY 2021-22']].copy()
     df.columns = ['city','state','Amount (Cr)']
     df = df.nlargest(10, 'Amount (Cr)')
     df['City_State'] = df['city'] + ', ' + df['state']
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Amount (Cr):Q', title='Amount Released (Cr)'),
         y=alt.Y('City_State:N', sort='-x', title='City, State'),
         color=alt.Color('state:N', title='State'),
         tooltip=['city:N','state:N', alt.Tooltip('Amount (Cr):Q', format='.1f')]
     ).properties(title='Top 10 Cities by NCAP Funding – FY 2021-22', width=500, height=400)
     return chart


</code>
"
507,spatio_temporal_aggregation,Show the monthly average PM2.5 for Himachal Pradesh across each year from 2017 to 2024 as small multiples (faceted by year).,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Himachal Pradesh'].copy()
    df['Year'] = df['Timestamp'].dt.year
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='steelblue').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5'),
        tooltip=['Year:O','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).facet(
        facet=alt.Facet('Year:O', title='Year'),
        columns=4
    ).properties(title='Monthly PM2.5 in Himachal Pradesh by Year (2017–2024)')
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Himachal Pradesh'].copy()
     df['Year'] = df['Timestamp'].dt.year
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='steelblue').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5'),
         tooltip=['Year:O','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).facet(
         facet=alt.Facet('Year:O', title='Year'),
         columns=4
     ).properties(title='Monthly PM2.5 in Himachal Pradesh by Year (2017–2024)')
     return chart


</code>
"
508,temporal_aggregation,Show a monthly bar chart of the number of days Madhya Pradesh exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2023.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Madhya Pradesh') & (data['Timestamp'].dt.year == 2023)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Madhya Pradesh Exceeded WHO PM2.5 Guideline per Month – 2023', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Madhya Pradesh') & (data['Timestamp'].dt.year == 2023)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Madhya Pradesh Exceeded WHO PM2.5 Guideline per Month – 2023', width=500, height=300)
     return chart


</code>
"
509,spatial_aggregation,Plot the average PM2.5 across all states in February 2017 as a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['Timestamp'].dt.year == 2017) & (data['Timestamp'].dt.month == 2)]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('PM2.5', ascending=False)
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='plasma'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Average PM2.5 by State – February 2017', width=500, height=400)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['Timestamp'].dt.year == 2017) & (data['Timestamp'].dt.month == 2)]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('PM2.5', ascending=False)
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='plasma'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Average PM2.5 by State – February 2017', width=500, height=400)
     return chart


</code>
"
510,specific_pattern,Plot the rolling 30-day average PM2.5 for West Bengal in 2018 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'West Bengal') & (data['Timestamp'].dt.year == 2018)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – West Bengal 2018', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'West Bengal') & (data['Timestamp'].dt.year == 2018)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – West Bengal 2018', width=600, height=300)


</code>
"
511,spatial_aggregation,"Show the top 14 states by average PM10 in 2018 as a bar chart, sorted in descending order.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2018]
    df = df.groupby('state')['PM10'].mean().reset_index().dropna()
    df = df.nlargest(14, 'PM10')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Top 14 States by Average PM10 in 2018', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2018]
     df = df.groupby('state')['PM10'].mean().reset_index().dropna()
     df = df.nlargest(14, 'PM10')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Top 14 States by Average PM10 in 2018', width=500, height=300)
     return chart


</code>
"
512,specific_pattern,Plot the rolling 30-day average PM2.5 for Madhya Pradesh in 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Madhya Pradesh') & (data['Timestamp'].dt.year == 2024)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Madhya Pradesh 2024', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Madhya Pradesh') & (data['Timestamp'].dt.year == 2024)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Madhya Pradesh 2024', width=600, height=300)


</code>
"
513,spatial_aggregation,Visualize the bottom 6 states with the lowest average PM2.5 in 2018 using a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2018]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nsmallest(6, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Bottom 6 States by Average PM2.5 in 2018', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2018]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nsmallest(6, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Bottom 6 States by Average PM2.5 in 2018', width=500, height=300)
     return chart


</code>
"
514,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Jharkhand, Odisha, and Karnataka in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Jharkhand', 'Odisha', 'Karnataka']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Jharkhand, Odisha, UP – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Jharkhand', 'Odisha', 'Karnataka']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Jharkhand, Odisha, UP – 2024', width=550, height=320)
     return chart


</code>
"
515,area_based,Show the PM2.5 per 1000 km² (air quality density) for each state in 2017 as a bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    pm = data[data['Timestamp'].dt.year == 2017].groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = pm.merge(states_data[['state','area (km2)']], on='state')
    df['PM2.5 per 1000 km²'] = df['PM2.5'] / df['area (km2)'] * 1000
    df = df.sort_values('PM2.5 per 1000 km²', ascending=False)
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5 per 1000 km²:Q', title='PM2.5 per 1000 km²'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM2\.5 per 1000 km²:Q', scale=alt.Scale(scheme='orangered'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5 per 1000 km²:Q', format='.3f')]
    ).properties(title='Air Quality Density (PM2.5 per 1000 km²) by State – 2017', width=500, height=400)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     pm = data[data['Timestamp'].dt.year == 2017].groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = pm.merge(states_data[['state','area (km2)']], on='state')
     df['PM2.5 per 1000 km²'] = df['PM2.5'] / df['area (km2)'] * 1000
     df = df.sort_values('PM2.5 per 1000 km²', ascending=False)
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5 per 1000 km²:Q', title='PM2.5 per 1000 km²'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM2\.5 per 1000 km²:Q', scale=alt.Scale(scheme='orangered'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5 per 1000 km²:Q', format='.3f')]
     ).properties(title='Air Quality Density (PM2.5 per 1000 km²) by State – 2017', width=500, height=400)
     return chart


</code>
"
516,specific_pattern,Plot the rolling 30-day average PM2.5 for West Bengal in 2020 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'West Bengal') & (data['Timestamp'].dt.year == 2020)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – West Bengal 2020', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'West Bengal') & (data['Timestamp'].dt.year == 2020)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – West Bengal 2020', width=600, height=300)


</code>
"
517,spatio_temporal_aggregation,Show a heatmap of average PM2.5 for the top 12 most polluted states by month for 2019.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    top8 = data.groupby('state')['PM2.5'].mean().nlargest(12).index.tolist()
    df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_rect().encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('state:N', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
        tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Heatmap – Top 12 Polluted States by Month (2019)', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     top8 = data.groupby('state')['PM2.5'].mean().nlargest(12).index.tolist()
     df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_rect().encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('state:N', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
         tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Heatmap – Top 12 Polluted States by Month (2019)', width=500, height=300)
     return chart


</code>
"
518,spatial_aggregation,Show a bar chart of the top 7 cities by median PM2.5 in 2021.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2021]
    df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
    df = df.nlargest(7, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
        y=alt.Y('city:N', sort='-x', title='City'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
        tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 7 Cities by Median PM2.5 in 2021', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2021]
     df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
     df = df.nlargest(7, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
         y=alt.Y('city:N', sort='-x', title='City'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
         tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 7 Cities by Median PM2.5 in 2021', width=500, height=300)
     return chart


</code>
"
519,temporal_aggregation,Plot the monthly average PM2.5 trend for Andhra Pradesh from 2017 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Andhra Pradesh'].copy()
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='steelblue').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly Average PM2.5 Trend – Andhra Pradesh (2017–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Andhra Pradesh'].copy()
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='steelblue').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly Average PM2.5 Trend – Andhra Pradesh (2017–2024)', width=600, height=300)
     return chart


</code>
"
520,temporal_aggregation,Plot the monthly average PM2.5 trend for Haryana from 2017 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Haryana'].copy()
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='steelblue').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly Average PM2.5 Trend – Haryana (2017–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Haryana'].copy()
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='steelblue').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly Average PM2.5 Trend – Haryana (2017–2024)', width=600, height=300)
     return chart


</code>
"
521,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Katihar, Haldia, and Ramanagara in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Katihar', 'Haldia', 'Ramanagara']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Katihar vs Haldia vs Ramanagara – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Katihar', 'Haldia', 'Ramanagara']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Katihar vs Haldia vs Ramanagara – 2023', width=550, height=320)
     return chart


</code>
"
522,specific_pattern,Show a cumulative area chart of PM2.5 readings for Gorakhpur across 2021.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Gorakhpur') & (data['Timestamp'].dt.year == 2021)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Gorakhpur 2021', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Gorakhpur') & (data['Timestamp'].dt.year == 2021)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Gorakhpur 2021', width=600, height=300)
     return chart


</code>
"
523,spatial_aggregation,Show a bar chart of the top 7 cities by median PM2.5 in 2020.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2020]
    df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
    df = df.nlargest(7, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
        y=alt.Y('city:N', sort='-x', title='City'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
        tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 7 Cities by Median PM2.5 in 2020', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2020]
     df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
     df = df.nlargest(7, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
         y=alt.Y('city:N', sort='-x', title='City'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
         tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 7 Cities by Median PM2.5 in 2020', width=500, height=300)
     return chart


</code>
"
524,specific_pattern,Plot the rolling 30-day average PM2.5 for Jammu and Kashmir in 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Jammu and Kashmir') & (data['Timestamp'].dt.year == 2024)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Jammu and Kashmir 2024', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Jammu and Kashmir') & (data['Timestamp'].dt.year == 2024)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Jammu and Kashmir 2024', width=600, height=300)


</code>
"
525,specific_pattern,Show PM2.5 exceedances above the Indian standard (60 µg/m³) per year as a bar chart for Jharkhand.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Jharkhand'].dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 60].copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby('Year')['Timestamp'].nunique().reset_index()
    df.columns = ['Year','Days Exceeded']
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('Days Exceeded:Q', title='Days PM2.5 > 60 µg/m³'),
        tooltip=['Year:N','Days Exceeded:Q']
    ).properties(title='Days Jharkhand Exceeded Indian PM2.5 Standard (60 µg/m³) per Year', width=450, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Jharkhand'].dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 60].copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby('Year')['Timestamp'].nunique().reset_index()
     df.columns = ['Year','Days Exceeded']
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('Days Exceeded:Q', title='Days PM2.5 > 60 µg/m³'),
         tooltip=['Year:N','Days Exceeded:Q']
     ).properties(title='Days Jharkhand Exceeded Indian PM2.5 Standard (60 µg/m³) per Year', width=450, height=300)
     return chart


</code>
"
526,spatio_temporal_aggregation,Show the monthly average PM2.5 for Uttar Pradesh across each year from 2017 to 2024 as small multiples (faceted by year).,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Uttar Pradesh'].copy()
    df['Year'] = df['Timestamp'].dt.year
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='steelblue').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5'),
        tooltip=['Year:O','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).facet(
        facet=alt.Facet('Year:O', title='Year'),
        columns=4
    ).properties(title='Monthly PM2.5 in Uttar Pradesh by Year (2017–2024)')
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Uttar Pradesh'].copy()
     df['Year'] = df['Timestamp'].dt.year
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='steelblue').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5'),
         tooltip=['Year:O','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).facet(
         facet=alt.Facet('Year:O', title='Year'),
         columns=4
     ).properties(title='Monthly PM2.5 in Uttar Pradesh by Year (2017–2024)')
     return chart


</code>
"
527,spatio_temporal_aggregation,"Create a faceted bar chart showing top 15 states by average PM2.5 per year for 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year.isin([2019,2020,2021,2022])].copy()
    df['Year'] = df['Timestamp'].dt.year
    agg = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    top = agg.groupby('Year').apply(lambda x: x.nlargest(15,'PM2.5')).reset_index(drop=True)
    chart = alt.Chart(top).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Avg PM2.5'),
        y=alt.Y('state:N', sort='-x'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).facet(facet='Year:O', columns=2).properties(title='Top 15 States by PM2.5 per Year')
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year.isin([2019,2020,2021,2022])].copy()
     df['Year'] = df['Timestamp'].dt.year
     agg = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     top = agg.groupby('Year').apply(lambda x: x.nlargest(15,'PM2.5')).reset_index(drop=True)
     chart = alt.Chart(top).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Avg PM2.5'),
         y=alt.Y('state:N', sort='-x'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).facet(facet='Year:O', columns=2).properties(title='Top 15 States by PM2.5 per Year')
     return chart


</code>
"
528,temporal_aggregation,Show the monthly average PM10 trend for Sivasagar from 2017 to 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Sivasagar'].copy()
    df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Sivasagar (2017–2022)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Sivasagar'].copy()
     df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Sivasagar (2017–2022)', width=600, height=300)
     return chart


</code>
"
529,spatial_aggregation,"Show the top 13 states by average PM10 in 2020 as a bar chart, sorted in descending order.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2020]
    df = df.groupby('state')['PM10'].mean().reset_index().dropna()
    df = df.nlargest(13, 'PM10')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Top 13 States by Average PM10 in 2020', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2020]
     df = df.groupby('state')['PM10'].mean().reset_index().dropna()
     df = df.nlargest(13, 'PM10')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Top 13 States by Average PM10 in 2020', width=500, height=300)
     return chart


</code>
"
530,spatio_temporal_aggregation,Create a grouped bar chart comparing the average PM2.5 in Winter vs Summer for the top 13 most polluted states.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    top6 = data.groupby('state')['PM2.5'].mean().nlargest(13).index.tolist()
    df = data[data['state'].isin(top6)].copy()
    df['Season'] = df['Timestamp'].dt.month.apply(
        lambda m: 'Winter' if m in [12,1,2] else ('Summer' if m in [3,4,5] else None))
    df = df.dropna(subset=['Season'])
    df = df.groupby(['state','Season'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('state:N', title='State'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('Season:N', title='Season'),
        xOffset='Season:N',
        tooltip=['state:N','Season:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Winter vs Summer PM2.5 – Top 13 Polluted States', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     top6 = data.groupby('state')['PM2.5'].mean().nlargest(13).index.tolist()
     df = data[data['state'].isin(top6)].copy()
     df['Season'] = df['Timestamp'].dt.month.apply(
         lambda m: 'Winter' if m in [12,1,2] else ('Summer' if m in [3,4,5] else None))
     df = df.dropna(subset=['Season'])
     df = df.groupby(['state','Season'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('state:N', title='State'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('Season:N', title='Season'),
         xOffset='Season:N',
         tooltip=['state:N','Season:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Winter vs Summer PM2.5 – Top 13 Polluted States', width=550, height=320)
     return chart


</code>
"
531,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Mizoram stations in 2024, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Mizoram') & (data['Timestamp'].dt.year == 2024)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Mizoram Stations 2024', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Mizoram') & (data['Timestamp'].dt.year == 2024)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Mizoram Stations 2024', width=450, height=350)


</code>
"
532,spatial_aggregation,Show a bar chart of the top 10 cities by median PM2.5 in 2018.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2018]
    df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
    df = df.nlargest(10, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
        y=alt.Y('city:N', sort='-x', title='City'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
        tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 10 Cities by Median PM2.5 in 2018', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2018]
     df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
     df = df.nlargest(10, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
         y=alt.Y('city:N', sort='-x', title='City'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
         tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 10 Cities by Median PM2.5 in 2018', width=500, height=300)
     return chart


</code>
"
533,specific_pattern,Plot the rolling 30-day average PM2.5 for Jharkhand in 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Jharkhand') & (data['Timestamp'].dt.year == 2022)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Jharkhand 2022', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Jharkhand') & (data['Timestamp'].dt.year == 2022)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Jharkhand 2022', width=600, height=300)


</code>
"
534,temporal_aggregation,Show the monthly average PM10 trend for Chennai from 2017 to 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Chennai'].copy()
    df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Chennai (2017–2022)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Chennai'].copy()
     df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Chennai (2017–2022)', width=600, height=300)
     return chart


</code>
"
535,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Telangana, Manipur, and Bihar across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Telangana', 'Manipur', 'Bihar']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Telangana', 'Manipur', 'Bihar']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
536,spatio_temporal_aggregation,"Create a faceted bar chart showing top 7 states by average PM2.5 per year for 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year.isin([2019,2020,2021,2022])].copy()
    df['Year'] = df['Timestamp'].dt.year
    agg = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    top = agg.groupby('Year').apply(lambda x: x.nlargest(7,'PM2.5')).reset_index(drop=True)
    chart = alt.Chart(top).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Avg PM2.5'),
        y=alt.Y('state:N', sort='-x'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).facet(facet='Year:O', columns=2).properties(title='Top 7 States by PM2.5 per Year')
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year.isin([2019,2020,2021,2022])].copy()
     df['Year'] = df['Timestamp'].dt.year
     agg = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     top = agg.groupby('Year').apply(lambda x: x.nlargest(7,'PM2.5')).reset_index(drop=True)
     chart = alt.Chart(top).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Avg PM2.5'),
         y=alt.Y('state:N', sort='-x'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).facet(facet='Year:O', columns=2).properties(title='Top 7 States by PM2.5 per Year')
     return chart


</code>
"
537,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Jharkhand, Manipur, and West Bengal from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Jharkhand', 'Manipur', 'West Bengal']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Jharkhand vs Manipur vs West Bengal', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Jharkhand', 'Manipur', 'West Bengal']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Jharkhand vs Manipur vs West Bengal', width=550, height=320)
     return chart


</code>
"
538,spatio_temporal_aggregation,Show the monthly average PM2.5 for Tripura across each year from 2017 to 2024 as small multiples (faceted by year).,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Tripura'].copy()
    df['Year'] = df['Timestamp'].dt.year
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='steelblue').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5'),
        tooltip=['Year:O','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).facet(
        facet=alt.Facet('Year:O', title='Year'),
        columns=4
    ).properties(title='Monthly PM2.5 in Tripura by Year (2017–2024)')
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Tripura'].copy()
     df['Year'] = df['Timestamp'].dt.year
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='steelblue').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5'),
         tooltip=['Year:O','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).facet(
         facet=alt.Facet('Year:O', title='Year'),
         columns=4
     ).properties(title='Monthly PM2.5 in Tripura by Year (2017–2024)')
     return chart


</code>
"
539,temporal_aggregation,Show the monthly average PM2.5 for Vijayapura in 2023 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Vijayapura') & (data['Timestamp'].dt.year == 2023)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Vijayapura 2023', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Vijayapura') & (data['Timestamp'].dt.year == 2023)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Vijayapura 2023', width=450, height=280)


</code>
"
540,temporal_aggregation,Show a monthly bar chart of the number of days West Bengal exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2022.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'West Bengal') & (data['Timestamp'].dt.year == 2022)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days West Bengal Exceeded WHO PM2.5 Guideline per Month – 2022', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'West Bengal') & (data['Timestamp'].dt.year == 2022)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days West Bengal Exceeded WHO PM2.5 Guideline per Month – 2022', width=500, height=300)
     return chart


</code>
"
541,temporal_aggregation,Show a monthly bar chart of the number of days Nagaland exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2023.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Nagaland') & (data['Timestamp'].dt.year == 2023)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Nagaland Exceeded WHO PM2.5 Guideline per Month – 2023', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Nagaland') & (data['Timestamp'].dt.year == 2023)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Nagaland Exceeded WHO PM2.5 Guideline per Month – 2023', width=500, height=300)
     return chart


</code>
"
542,spatial_aggregation,"Show the top 5 states by average PM10 in 2017 as a bar chart, sorted in descending order.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2017]
    df = df.groupby('state')['PM10'].mean().reset_index().dropna()
    df = df.nlargest(5, 'PM10')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Top 5 States by Average PM10 in 2017', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2017]
     df = df.groupby('state')['PM10'].mean().reset_index().dropna()
     df = df.nlargest(5, 'PM10')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Top 5 States by Average PM10 in 2017', width=500, height=300)
     return chart


</code>
"
543,temporal_aggregation,Plot the weekly average PM2.5 for Perundurai in 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Perundurai') & (data['Timestamp'].dt.year == 2024)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Perundurai 2024', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Perundurai') & (data['Timestamp'].dt.year == 2024)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Perundurai 2024', width=600, height=300)
     return chart


</code>
"
544,spatial_aggregation,"Show the top 15 states by average PM10 in 2021 as a bar chart, sorted in descending order.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2021]
    df = df.groupby('state')['PM10'].mean().reset_index().dropna()
    df = df.nlargest(15, 'PM10')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Top 15 States by Average PM10 in 2021', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2021]
     df = df.groupby('state')['PM10'].mean().reset_index().dropna()
     df = df.nlargest(15, 'PM10')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Top 15 States by Average PM10 in 2021', width=500, height=300)
     return chart


</code>
"
545,spatio_temporal_aggregation,"Create a faceted bar chart showing top 8 states by average PM2.5 per year for 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year.isin([2021,2022,2023,2024])].copy()
    df['Year'] = df['Timestamp'].dt.year
    agg = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    top = agg.groupby('Year').apply(lambda x: x.nlargest(8,'PM2.5')).reset_index(drop=True)
    chart = alt.Chart(top).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Avg PM2.5'),
        y=alt.Y('state:N', sort='-x'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).facet(facet='Year:O', columns=2).properties(title='Top 8 States by PM2.5 per Year')
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year.isin([2021,2022,2023,2024])].copy()
     df['Year'] = df['Timestamp'].dt.year
     agg = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     top = agg.groupby('Year').apply(lambda x: x.nlargest(8,'PM2.5')).reset_index(drop=True)
     chart = alt.Chart(top).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Avg PM2.5'),
         y=alt.Y('state:N', sort='-x'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).facet(facet='Year:O', columns=2).properties(title='Top 8 States by PM2.5 per Year')
     return chart


</code>
"
546,specific_pattern,Plot the rolling 30-day average PM2.5 for Himachal Pradesh in 2020 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Himachal Pradesh') & (data['Timestamp'].dt.year == 2020)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Himachal Pradesh 2020', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Himachal Pradesh') & (data['Timestamp'].dt.year == 2020)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Himachal Pradesh 2020', width=600, height=300)


</code>
"
547,spatio_temporal_aggregation,Show a heatmap of average PM2.5 for the top 13 most polluted states by month for 2022.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    top8 = data.groupby('state')['PM2.5'].mean().nlargest(13).index.tolist()
    df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_rect().encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('state:N', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
        tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Heatmap – Top 13 Polluted States by Month (2022)', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     top8 = data.groupby('state')['PM2.5'].mean().nlargest(13).index.tolist()
     df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_rect().encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('state:N', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
         tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Heatmap – Top 13 Polluted States by Month (2022)', width=500, height=300)
     return chart


</code>
"
548,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Haryana, Bihar, and Madhya Pradesh in 2021 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Haryana', 'Bihar', 'Madhya Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Haryana, Bihar, UP – 2021', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Haryana', 'Bihar', 'Madhya Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Haryana, Bihar, UP – 2021', width=550, height=320)
     return chart


</code>
"
549,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Chhattisgarh, Telangana, and Telangana in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Chhattisgarh', 'Telangana', 'Telangana']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Chhattisgarh, Telangana, UP – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Chhattisgarh', 'Telangana', 'Telangana']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Chhattisgarh, Telangana, UP – 2024', width=550, height=320)
     return chart


</code>
"
550,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Jharkhand, Mizoram, and Arunachal Pradesh in 2018 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Jharkhand', 'Mizoram', 'Arunachal Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2018)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Jharkhand, Mizoram, UP – 2018', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Jharkhand', 'Mizoram', 'Arunachal Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2018)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Jharkhand, Mizoram, UP – 2018', width=550, height=320)
     return chart


</code>
"
551,funding_based,Visualize NCAP city-level funding for FY 2021-22 as a horizontal bar chart for the top 6 cities.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = ncap_funding_data[['city','state','Amount released during FY 2021-22']].copy()
    df.columns = ['city','state','Amount (Cr)']
    df = df.nlargest(6, 'Amount (Cr)')
    df['City_State'] = df['city'] + ', ' + df['state']
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Amount (Cr):Q', title='Amount Released (Cr)'),
        y=alt.Y('City_State:N', sort='-x', title='City, State'),
        color=alt.Color('state:N', title='State'),
        tooltip=['city:N','state:N', alt.Tooltip('Amount (Cr):Q', format='.1f')]
    ).properties(title='Top 6 Cities by NCAP Funding – FY 2021-22', width=500, height=400)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = ncap_funding_data[['city','state','Amount released during FY 2021-22']].copy()
     df.columns = ['city','state','Amount (Cr)']
     df = df.nlargest(6, 'Amount (Cr)')
     df['City_State'] = df['city'] + ', ' + df['state']
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Amount (Cr):Q', title='Amount Released (Cr)'),
         y=alt.Y('City_State:N', sort='-x', title='City, State'),
         color=alt.Color('state:N', title='State'),
         tooltip=['city:N','state:N', alt.Tooltip('Amount (Cr):Q', format='.1f')]
     ).properties(title='Top 6 Cities by NCAP Funding – FY 2021-22', width=500, height=400)
     return chart


</code>
"
552,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Uttar Pradesh, Rajasthan, and Haryana across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Uttar Pradesh', 'Rajasthan', 'Haryana']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Uttar Pradesh', 'Rajasthan', 'Haryana']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
553,spatio_temporal_aggregation,Show a heatmap of average PM2.5 for the top 11 most polluted states by month for 2024.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    top8 = data.groupby('state')['PM2.5'].mean().nlargest(11).index.tolist()
    df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_rect().encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('state:N', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
        tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Heatmap – Top 11 Polluted States by Month (2024)', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     top8 = data.groupby('state')['PM2.5'].mean().nlargest(11).index.tolist()
     df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_rect().encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('state:N', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
         tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Heatmap – Top 11 Polluted States by Month (2024)', width=500, height=300)
     return chart


</code>
"
554,spatio_temporal_aggregation,Show a heatmap of average PM2.5 for the top 11 most polluted states by month for 2017.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    top8 = data.groupby('state')['PM2.5'].mean().nlargest(11).index.tolist()
    df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2017)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_rect().encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('state:N', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
        tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Heatmap – Top 11 Polluted States by Month (2017)', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     top8 = data.groupby('state')['PM2.5'].mean().nlargest(11).index.tolist()
     df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2017)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_rect().encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('state:N', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
         tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Heatmap – Top 11 Polluted States by Month (2017)', width=500, height=300)
     return chart


</code>
"
555,spatio_temporal_aggregation,Show a heatmap of average PM2.5 for the top 9 most polluted states by month for 2022.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    top8 = data.groupby('state')['PM2.5'].mean().nlargest(9).index.tolist()
    df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_rect().encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('state:N', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
        tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Heatmap – Top 9 Polluted States by Month (2022)', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     top8 = data.groupby('state')['PM2.5'].mean().nlargest(9).index.tolist()
     df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_rect().encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('state:N', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
         tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Heatmap – Top 9 Polluted States by Month (2022)', width=500, height=300)
     return chart


</code>
"
556,spatio_temporal_aggregation,Show a heatmap of average PM2.5 for the top 10 most polluted states by month for 2024.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    top8 = data.groupby('state')['PM2.5'].mean().nlargest(10).index.tolist()
    df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_rect().encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('state:N', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
        tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Heatmap – Top 10 Polluted States by Month (2024)', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     top8 = data.groupby('state')['PM2.5'].mean().nlargest(10).index.tolist()
     df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_rect().encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('state:N', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
         tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Heatmap – Top 10 Polluted States by Month (2024)', width=500, height=300)
     return chart


</code>
"
557,spatial_aggregation,Visualize the bottom 5 states with the lowest average PM2.5 in 2017 using a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2017]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nsmallest(5, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Bottom 5 States by Average PM2.5 in 2017', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2017]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nsmallest(5, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Bottom 5 States by Average PM2.5 in 2017', width=500, height=300)
     return chart


</code>
"
558,temporal_aggregation,Show the monthly average PM10 trend for Satna from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Satna'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Satna (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Satna'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Satna (2019–2024)', width=600, height=300)
     return chart


</code>
"
559,temporal_aggregation,Show the monthly average PM10 trend for Kurukshetra from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Kurukshetra'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Kurukshetra (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Kurukshetra'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Kurukshetra (2019–2024)', width=600, height=300)
     return chart


</code>
"
560,temporal_aggregation,Show a monthly bar chart of the number of days Nagaland exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2021.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Nagaland') & (data['Timestamp'].dt.year == 2021)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Nagaland Exceeded WHO PM2.5 Guideline per Month – 2021', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Nagaland') & (data['Timestamp'].dt.year == 2021)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Nagaland Exceeded WHO PM2.5 Guideline per Month – 2021', width=500, height=300)
     return chart


</code>
"
561,spatio_temporal_aggregation,"Create a faceted bar chart showing top 13 states by average PM2.5 per year for 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year.isin([2020,2021,2022,2023])].copy()
    df['Year'] = df['Timestamp'].dt.year
    agg = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    top = agg.groupby('Year').apply(lambda x: x.nlargest(13,'PM2.5')).reset_index(drop=True)
    chart = alt.Chart(top).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Avg PM2.5'),
        y=alt.Y('state:N', sort='-x'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).facet(facet='Year:O', columns=2).properties(title='Top 13 States by PM2.5 per Year')
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year.isin([2020,2021,2022,2023])].copy()
     df['Year'] = df['Timestamp'].dt.year
     agg = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     top = agg.groupby('Year').apply(lambda x: x.nlargest(13,'PM2.5')).reset_index(drop=True)
     chart = alt.Chart(top).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Avg PM2.5'),
         y=alt.Y('state:N', sort='-x'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).facet(facet='Year:O', columns=2).properties(title='Top 13 States by PM2.5 per Year')
     return chart


</code>
"
562,spatio_temporal_aggregation,Show a heatmap of average PM2.5 for the top 15 most polluted states by month for 2019.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    top8 = data.groupby('state')['PM2.5'].mean().nlargest(15).index.tolist()
    df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_rect().encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('state:N', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
        tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Heatmap – Top 15 Polluted States by Month (2019)', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     top8 = data.groupby('state')['PM2.5'].mean().nlargest(15).index.tolist()
     df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_rect().encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('state:N', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
         tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Heatmap – Top 15 Polluted States by Month (2019)', width=500, height=300)
     return chart


</code>
"
563,temporal_aggregation,Plot the monthly average PM2.5 trend for Kerala from 2017 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Kerala'].copy()
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='steelblue').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly Average PM2.5 Trend – Kerala (2017–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Kerala'].copy()
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='steelblue').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly Average PM2.5 Trend – Kerala (2017–2024)', width=600, height=300)
     return chart


</code>
"
564,specific_pattern,Show a cumulative area chart of PM2.5 readings for Angul across 2023.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Angul') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Angul 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Angul') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Angul 2023', width=600, height=300)
     return chart


</code>
"
565,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Shivamogga, Ballabgarh, and Howrah in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Shivamogga', 'Ballabgarh', 'Howrah']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Shivamogga vs Ballabgarh vs Howrah – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Shivamogga', 'Ballabgarh', 'Howrah']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Shivamogga vs Ballabgarh vs Howrah – 2023', width=550, height=320)
     return chart


</code>
"
566,temporal_aggregation,Show a monthly bar chart of the number of days Odisha exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2022.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Odisha') & (data['Timestamp'].dt.year == 2022)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Odisha Exceeded WHO PM2.5 Guideline per Month – 2022', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Odisha') & (data['Timestamp'].dt.year == 2022)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Odisha Exceeded WHO PM2.5 Guideline per Month – 2022', width=500, height=300)
     return chart


</code>
"
567,spatial_aggregation,"Create a scatter plot of PM2.5 vs PM10 for all stations in 2018, colored by state.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2018]
    df = df.groupby(['station','state'])[['PM2.5','PM10']].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_point(opacity=0.6, size=60).encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['station:N','state:N',
                 alt.Tooltip('PM2\.5:Q', format='.1f'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='PM2.5 vs PM10 by Station – 2018', width=500, height=400)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2018]
     df = df.groupby(['station','state'])[['PM2.5','PM10']].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_point(opacity=0.6, size=60).encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['station:N','state:N',
                  alt.Tooltip('PM2\.5:Q', format='.1f'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='PM2.5 vs PM10 by Station – 2018', width=500, height=400)
     return chart


</code>
"
568,specific_pattern,Plot the rolling 30-day average PM2.5 for Arunachal Pradesh in 2018 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Arunachal Pradesh') & (data['Timestamp'].dt.year == 2018)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Arunachal Pradesh 2018', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Arunachal Pradesh') & (data['Timestamp'].dt.year == 2018)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Arunachal Pradesh 2018', width=600, height=300)


</code>
"
569,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Kerala stations in 2018, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Kerala') & (data['Timestamp'].dt.year == 2018)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Kerala Stations 2018', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Kerala') & (data['Timestamp'].dt.year == 2018)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Kerala Stations 2018', width=450, height=350)


</code>
"
570,specific_pattern,Plot the rolling 30-day average PM2.5 for Tamil Nadu in 2023 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Tamil Nadu') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Tamil Nadu 2023', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Tamil Nadu') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Tamil Nadu 2023', width=600, height=300)


</code>
"
571,temporal_aggregation,Show the monthly average PM2.5 for Karnal in 2018 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Karnal') & (data['Timestamp'].dt.year == 2018)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Karnal 2018', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Karnal') & (data['Timestamp'].dt.year == 2018)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Karnal 2018', width=450, height=280)


</code>
"
572,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for West Bengal, Sikkim, and Himachal Pradesh from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['West Bengal', 'Sikkim', 'Himachal Pradesh']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: West Bengal vs Sikkim vs Himachal Pradesh', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['West Bengal', 'Sikkim', 'Himachal Pradesh']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: West Bengal vs Sikkim vs Himachal Pradesh', width=550, height=320)
     return chart


</code>
"
573,temporal_aggregation,Plot the weekly average PM2.5 for Sirohi in 2023 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Sirohi') & (data['Timestamp'].dt.year == 2023)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Sirohi 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Sirohi') & (data['Timestamp'].dt.year == 2023)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Sirohi 2023', width=600, height=300)
     return chart


</code>
"
574,spatio_temporal_aggregation,Create a heatmap showing the average PM2.5 by month (x-axis) and year (y-axis) for Maharashtra.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Maharashtra'].copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_rect().encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Year:N', title='Year'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='Avg PM2.5'),
        tooltip=['Year:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Heatmap – Maharashtra (Month × Year)', width=500, height=280)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Maharashtra'].copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_rect().encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Year:N', title='Year'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='Avg PM2.5'),
         tooltip=['Year:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Heatmap – Maharashtra (Month × Year)', width=500, height=280)
     return chart


</code>
"
575,temporal_aggregation,Plot the monthly average PM2.5 trend for Mizoram from 2017 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Mizoram'].copy()
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='steelblue').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly Average PM2.5 Trend – Mizoram (2017–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Mizoram'].copy()
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='steelblue').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly Average PM2.5 Trend – Mizoram (2017–2024)', width=600, height=300)
     return chart


</code>
"
576,specific_pattern,Plot the rolling 30-day average PM2.5 for Karnataka in 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Karnataka') & (data['Timestamp'].dt.year == 2022)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Karnataka 2022', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Karnataka') & (data['Timestamp'].dt.year == 2022)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Karnataka 2022', width=600, height=300)


</code>
"
577,spatio_temporal_aggregation,Show the monthly average PM2.5 for West Bengal across each year from 2017 to 2024 as small multiples (faceted by year).,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'West Bengal'].copy()
    df['Year'] = df['Timestamp'].dt.year
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='steelblue').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5'),
        tooltip=['Year:O','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).facet(
        facet=alt.Facet('Year:O', title='Year'),
        columns=4
    ).properties(title='Monthly PM2.5 in West Bengal by Year (2017–2024)')
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'West Bengal'].copy()
     df['Year'] = df['Timestamp'].dt.year
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='steelblue').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5'),
         tooltip=['Year:O','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).facet(
         facet=alt.Facet('Year:O', title='Year'),
         columns=4
     ).properties(title='Monthly PM2.5 in West Bengal by Year (2017–2024)')
     return chart


</code>
"
578,temporal_aggregation,Show the monthly average PM2.5 for Udupi in 2024 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Udupi') & (data['Timestamp'].dt.year == 2024)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Udupi 2024', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Udupi') & (data['Timestamp'].dt.year == 2024)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Udupi 2024', width=450, height=280)


</code>
"
579,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Odisha, Arunachal Pradesh, and Gujarat from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Odisha', 'Arunachal Pradesh', 'Gujarat']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Odisha vs Arunachal Pradesh vs Gujarat', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Odisha', 'Arunachal Pradesh', 'Gujarat']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Odisha vs Arunachal Pradesh vs Gujarat', width=550, height=320)
     return chart


</code>
"
580,specific_pattern,Plot the rolling 30-day average PM2.5 for Chandigarh in 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Chandigarh') & (data['Timestamp'].dt.year == 2024)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Chandigarh 2024', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Chandigarh') & (data['Timestamp'].dt.year == 2024)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Chandigarh 2024', width=600, height=300)


</code>
"
581,spatial_aggregation,Plot the top 7 states by average PM2.5 in 2022 as a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2022]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nlargest(7, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 7 States by Average PM2.5 in 2022', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2022]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nlargest(7, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 7 States by Average PM2.5 in 2022', width=500, height=300)
     return chart


</code>
"
582,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Uttarakhand stations in 2023, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Uttarakhand') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Uttarakhand Stations 2023', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Uttarakhand') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Uttarakhand Stations 2023', width=450, height=350)


</code>
"
583,temporal_aggregation,Show the monthly average PM2.5 for Katni in 2024 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Katni') & (data['Timestamp'].dt.year == 2024)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Katni 2024', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Katni') & (data['Timestamp'].dt.year == 2024)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Katni 2024', width=450, height=280)


</code>
"
584,spatio_temporal_aggregation,Create a heatmap showing the average PM2.5 by month (x-axis) and year (y-axis) for Himachal Pradesh.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Himachal Pradesh'].copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_rect().encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Year:N', title='Year'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='Avg PM2.5'),
        tooltip=['Year:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Heatmap – Himachal Pradesh (Month × Year)', width=500, height=280)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Himachal Pradesh'].copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_rect().encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Year:N', title='Year'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='Avg PM2.5'),
         tooltip=['Year:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Heatmap – Himachal Pradesh (Month × Year)', width=500, height=280)
     return chart


</code>
"
585,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Chhattisgarh stations in 2024, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Chhattisgarh') & (data['Timestamp'].dt.year == 2024)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Chhattisgarh Stations 2024', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Chhattisgarh') & (data['Timestamp'].dt.year == 2024)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Chhattisgarh Stations 2024', width=450, height=350)


</code>
"
586,spatial_aggregation,Visualize the bottom 11 states with the lowest average PM2.5 in 2024 using a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2024]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nsmallest(11, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Bottom 11 States by Average PM2.5 in 2024', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2024]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nsmallest(11, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Bottom 11 States by Average PM2.5 in 2024', width=500, height=300)
     return chart


</code>
"
587,temporal_aggregation,Show the monthly average PM10 trend for Kannur from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Kannur'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Kannur (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Kannur'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Kannur (2019–2024)', width=600, height=300)
     return chart


</code>
"
588,temporal_aggregation,Show the monthly average PM2.5 for Bundi in 2024 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Bundi') & (data['Timestamp'].dt.year == 2024)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Bundi 2024', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Bundi') & (data['Timestamp'].dt.year == 2024)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Bundi 2024', width=450, height=280)


</code>
"
589,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Malegaon, Alwar, and Fatehabad in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Malegaon', 'Alwar', 'Fatehabad']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Malegaon vs Alwar vs Fatehabad – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Malegaon', 'Alwar', 'Fatehabad']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Malegaon vs Alwar vs Fatehabad – 2023', width=550, height=320)
     return chart


</code>
"
590,spatio_temporal_aggregation,Show the monthly average PM2.5 for Mizoram across each year from 2017 to 2024 as small multiples (faceted by year).,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Mizoram'].copy()
    df['Year'] = df['Timestamp'].dt.year
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='steelblue').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5'),
        tooltip=['Year:O','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).facet(
        facet=alt.Facet('Year:O', title='Year'),
        columns=4
    ).properties(title='Monthly PM2.5 in Mizoram by Year (2017–2024)')
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Mizoram'].copy()
     df['Year'] = df['Timestamp'].dt.year
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='steelblue').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5'),
         tooltip=['Year:O','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).facet(
         facet=alt.Facet('Year:O', title='Year'),
         columns=4
     ).properties(title='Monthly PM2.5 in Mizoram by Year (2017–2024)')
     return chart


</code>
"
591,specific_pattern,Plot the rolling 30-day average PM2.5 for Delhi in 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Delhi') & (data['Timestamp'].dt.year == 2024)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Delhi 2024', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Delhi') & (data['Timestamp'].dt.year == 2024)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Delhi 2024', width=600, height=300)


</code>
"
592,spatial_aggregation,Plot the distribution of PM2.5 values in Uttar Pradesh across all years using a histogram.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Uttar Pradesh'].dropna(subset=['PM2.5'])
    df = df[['PM2.5']]
    chart = alt.Chart(df).mark_bar(color='steelblue', opacity=0.75).encode(
        x=alt.X('PM2\.5:Q', bin=alt.Bin(maxbins=40), title='PM2.5 (µg/m³)'),
        y=alt.Y('count()', title='Number of Observations'),
        tooltip=[alt.Tooltip('PM2\.5:Q', bin=True), 'count()']
    ).properties(title='PM2.5 Distribution – Uttar Pradesh (All Years)', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Uttar Pradesh'].dropna(subset=['PM2.5'])
     df = df[['PM2.5']]
     chart = alt.Chart(df).mark_bar(color='steelblue', opacity=0.75).encode(
         x=alt.X('PM2\.5:Q', bin=alt.Bin(maxbins=40), title='PM2.5 (µg/m³)'),
         y=alt.Y('count()', title='Number of Observations'),
         tooltip=[alt.Tooltip('PM2\.5:Q', bin=True), 'count()']
     ).properties(title='PM2.5 Distribution – Uttar Pradesh (All Years)', width=500, height=300)
     return chart


</code>
"
593,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Assam, Puducherry, and Karnataka across 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Assam', 'Puducherry', 'Karnataka']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Assam', 'Puducherry', 'Karnataka']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
     return chart


</code>
"
594,temporal_aggregation,Show the monthly average PM10 trend for Latur from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Latur'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Latur (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Latur'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Latur (2019–2024)', width=600, height=300)
     return chart


</code>
"
595,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Karnataka, Haryana, and Gujarat in 2017 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Karnataka', 'Haryana', 'Gujarat']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2017)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Karnataka, Haryana, UP – 2017', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Karnataka', 'Haryana', 'Gujarat']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2017)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Karnataka, Haryana, UP – 2017', width=550, height=320)
     return chart


</code>
"
596,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Baripada, Indore, and Baripada in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Baripada', 'Indore', 'Baripada']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Baripada vs Indore vs Baripada – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Baripada', 'Indore', 'Baripada']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Baripada vs Indore vs Baripada – 2023', width=550, height=320)
     return chart


</code>
"
597,temporal_aggregation,Show a monthly bar chart of the number of days Puducherry exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2021.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Puducherry') & (data['Timestamp'].dt.year == 2021)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Puducherry Exceeded WHO PM2.5 Guideline per Month – 2021', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Puducherry') & (data['Timestamp'].dt.year == 2021)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Puducherry Exceeded WHO PM2.5 Guideline per Month – 2021', width=500, height=300)
     return chart


</code>
"
598,spatio_temporal_aggregation,"Create a faceted bar chart showing top 15 states by average PM2.5 per year for 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year.isin([2017,2018,2019,2020])].copy()
    df['Year'] = df['Timestamp'].dt.year
    agg = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    top = agg.groupby('Year').apply(lambda x: x.nlargest(15,'PM2.5')).reset_index(drop=True)
    chart = alt.Chart(top).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Avg PM2.5'),
        y=alt.Y('state:N', sort='-x'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).facet(facet='Year:O', columns=2).properties(title='Top 15 States by PM2.5 per Year')
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year.isin([2017,2018,2019,2020])].copy()
     df['Year'] = df['Timestamp'].dt.year
     agg = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     top = agg.groupby('Year').apply(lambda x: x.nlargest(15,'PM2.5')).reset_index(drop=True)
     chart = alt.Chart(top).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Avg PM2.5'),
         y=alt.Y('state:N', sort='-x'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).facet(facet='Year:O', columns=2).properties(title='Top 15 States by PM2.5 per Year')
     return chart


</code>
"
599,spatio_temporal_aggregation,Show the monthly average PM2.5 for Jharkhand across each year from 2017 to 2024 as small multiples (faceted by year).,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Jharkhand'].copy()
    df['Year'] = df['Timestamp'].dt.year
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='steelblue').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5'),
        tooltip=['Year:O','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).facet(
        facet=alt.Facet('Year:O', title='Year'),
        columns=4
    ).properties(title='Monthly PM2.5 in Jharkhand by Year (2017–2024)')
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Jharkhand'].copy()
     df['Year'] = df['Timestamp'].dt.year
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='steelblue').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5'),
         tooltip=['Year:O','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).facet(
         facet=alt.Facet('Year:O', title='Year'),
         columns=4
     ).properties(title='Monthly PM2.5 in Jharkhand by Year (2017–2024)')
     return chart


</code>
"
600,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for West Bengal, Maharashtra, and Chandigarh from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['West Bengal', 'Maharashtra', 'Chandigarh']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: West Bengal vs Maharashtra vs Chandigarh', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['West Bengal', 'Maharashtra', 'Chandigarh']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: West Bengal vs Maharashtra vs Chandigarh', width=550, height=320)
     return chart


</code>
"
601,temporal_aggregation,Plot the weekly average PM2.5 for Jalna in 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Jalna') & (data['Timestamp'].dt.year == 2024)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Jalna 2024', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Jalna') & (data['Timestamp'].dt.year == 2024)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Jalna 2024', width=600, height=300)
     return chart


</code>
"
602,specific_pattern,Plot the rolling 30-day average PM2.5 for Nagaland in 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Nagaland') & (data['Timestamp'].dt.year == 2024)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Nagaland 2024', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Nagaland') & (data['Timestamp'].dt.year == 2024)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Nagaland 2024', width=600, height=300)


</code>
"
603,temporal_aggregation,Show the monthly average PM2.5 for Udaipur in 2022 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Udaipur') & (data['Timestamp'].dt.year == 2022)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Udaipur 2022', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Udaipur') & (data['Timestamp'].dt.year == 2022)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Udaipur 2022', width=450, height=280)


</code>
"
604,temporal_aggregation,Show a monthly bar chart of the number of days Tripura exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2020.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Tripura') & (data['Timestamp'].dt.year == 2020)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Tripura Exceeded WHO PM2.5 Guideline per Month – 2020', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Tripura') & (data['Timestamp'].dt.year == 2020)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Tripura Exceeded WHO PM2.5 Guideline per Month – 2020', width=500, height=300)
     return chart


</code>
"
605,spatio_temporal_aggregation,"Create a faceted bar chart showing top 11 states by average PM2.5 per year for 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year.isin([2017,2018,2019,2020])].copy()
    df['Year'] = df['Timestamp'].dt.year
    agg = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    top = agg.groupby('Year').apply(lambda x: x.nlargest(11,'PM2.5')).reset_index(drop=True)
    chart = alt.Chart(top).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Avg PM2.5'),
        y=alt.Y('state:N', sort='-x'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).facet(facet='Year:O', columns=2).properties(title='Top 11 States by PM2.5 per Year')
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year.isin([2017,2018,2019,2020])].copy()
     df['Year'] = df['Timestamp'].dt.year
     agg = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     top = agg.groupby('Year').apply(lambda x: x.nlargest(11,'PM2.5')).reset_index(drop=True)
     chart = alt.Chart(top).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Avg PM2.5'),
         y=alt.Y('state:N', sort='-x'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).facet(facet='Year:O', columns=2).properties(title='Top 11 States by PM2.5 per Year')
     return chart


</code>
"
606,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Belgaum, Hapur, and Agra in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Belgaum', 'Hapur', 'Agra']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Belgaum vs Hapur vs Agra – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Belgaum', 'Hapur', 'Agra']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Belgaum vs Hapur vs Agra – 2024', width=550, height=320)
     return chart


</code>
"
607,temporal_aggregation,Show the monthly average PM10 trend for Nagaur from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Nagaur'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Nagaur (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Nagaur'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Nagaur (2019–2024)', width=600, height=300)
     return chart


</code>
"
608,temporal_aggregation,Plot the monthly average PM2.5 trend for Chhattisgarh from 2017 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Chhattisgarh'].copy()
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='steelblue').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly Average PM2.5 Trend – Chhattisgarh (2017–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Chhattisgarh'].copy()
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='steelblue').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly Average PM2.5 Trend – Chhattisgarh (2017–2024)', width=600, height=300)
     return chart


</code>
"
609,temporal_aggregation,Show the monthly average PM10 trend for Agra from 2017 to 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Agra'].copy()
    df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Agra (2017–2022)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Agra'].copy()
     df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Agra (2017–2022)', width=600, height=300)
     return chart


</code>
"
610,spatial_aggregation,"Show the top 12 states by average PM10 in 2020 as a bar chart, sorted in descending order.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2020]
    df = df.groupby('state')['PM10'].mean().reset_index().dropna()
    df = df.nlargest(12, 'PM10')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Top 12 States by Average PM10 in 2020', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2020]
     df = df.groupby('state')['PM10'].mean().reset_index().dropna()
     df = df.nlargest(12, 'PM10')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Top 12 States by Average PM10 in 2020', width=500, height=300)
     return chart


</code>
"
611,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Telangana, Madhya Pradesh, and Gujarat across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Telangana', 'Madhya Pradesh', 'Gujarat']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Telangana', 'Madhya Pradesh', 'Gujarat']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
612,temporal_aggregation,Plot the monthly average PM2.5 trend for Arunachal Pradesh from 2017 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Arunachal Pradesh'].copy()
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='steelblue').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly Average PM2.5 Trend – Arunachal Pradesh (2017–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Arunachal Pradesh'].copy()
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='steelblue').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly Average PM2.5 Trend – Arunachal Pradesh (2017–2024)', width=600, height=300)
     return chart


</code>
"
613,spatial_aggregation,"Show the top 8 states by average PM10 in 2018 as a bar chart, sorted in descending order.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2018]
    df = df.groupby('state')['PM10'].mean().reset_index().dropna()
    df = df.nlargest(8, 'PM10')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Top 8 States by Average PM10 in 2018', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2018]
     df = df.groupby('state')['PM10'].mean().reset_index().dropna()
     df = df.nlargest(8, 'PM10')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Top 8 States by Average PM10 in 2018', width=500, height=300)
     return chart


</code>
"
614,spatial_aggregation,Show a bar chart of the top 14 cities by median PM2.5 in 2017.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2017]
    df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
    df = df.nlargest(14, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
        y=alt.Y('city:N', sort='-x', title='City'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
        tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 14 Cities by Median PM2.5 in 2017', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2017]
     df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
     df = df.nlargest(14, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
         y=alt.Y('city:N', sort='-x', title='City'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
         tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 14 Cities by Median PM2.5 in 2017', width=500, height=300)
     return chart


</code>
"
615,spatial_aggregation,Visualize the bottom 12 states with the lowest average PM2.5 in 2020 using a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2020]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nsmallest(12, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Bottom 12 States by Average PM2.5 in 2020', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2020]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nsmallest(12, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Bottom 12 States by Average PM2.5 in 2020', width=500, height=300)
     return chart


</code>
"
616,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Meghalaya, Sikkim, and Delhi from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Meghalaya', 'Sikkim', 'Delhi']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Meghalaya vs Sikkim vs Delhi', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Meghalaya', 'Sikkim', 'Delhi']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Meghalaya vs Sikkim vs Delhi', width=550, height=320)
     return chart


</code>
"
617,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Telangana, Rajasthan, and Uttarakhand from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Telangana', 'Rajasthan', 'Uttarakhand']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Telangana vs Rajasthan vs Uttarakhand', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Telangana', 'Rajasthan', 'Uttarakhand']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Telangana vs Rajasthan vs Uttarakhand', width=550, height=320)
     return chart


</code>
"
618,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Chandigarh, Puducherry, and Puducherry in 2019 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Chandigarh', 'Puducherry', 'Puducherry']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Chandigarh, Puducherry, UP – 2019', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Chandigarh', 'Puducherry', 'Puducherry']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Chandigarh, Puducherry, UP – 2019', width=550, height=320)
     return chart


</code>
"
619,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Haryana stations in 2019, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Haryana') & (data['Timestamp'].dt.year == 2019)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Haryana Stations 2019', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Haryana') & (data['Timestamp'].dt.year == 2019)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Haryana Stations 2019', width=450, height=350)


</code>
"
620,spatial_aggregation,Plot the distribution of PM2.5 values in Puducherry across all years using a histogram.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Puducherry'].dropna(subset=['PM2.5'])
    df = df[['PM2.5']]
    chart = alt.Chart(df).mark_bar(color='steelblue', opacity=0.75).encode(
        x=alt.X('PM2\.5:Q', bin=alt.Bin(maxbins=40), title='PM2.5 (µg/m³)'),
        y=alt.Y('count()', title='Number of Observations'),
        tooltip=[alt.Tooltip('PM2\.5:Q', bin=True), 'count()']
    ).properties(title='PM2.5 Distribution – Puducherry (All Years)', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Puducherry'].dropna(subset=['PM2.5'])
     df = df[['PM2.5']]
     chart = alt.Chart(df).mark_bar(color='steelblue', opacity=0.75).encode(
         x=alt.X('PM2\.5:Q', bin=alt.Bin(maxbins=40), title='PM2.5 (µg/m³)'),
         y=alt.Y('count()', title='Number of Observations'),
         tooltip=[alt.Tooltip('PM2\.5:Q', bin=True), 'count()']
     ).properties(title='PM2.5 Distribution – Puducherry (All Years)', width=500, height=300)
     return chart


</code>
"
621,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Arunachal Pradesh, West Bengal, and Jharkhand across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Arunachal Pradesh', 'West Bengal', 'Jharkhand']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Arunachal Pradesh', 'West Bengal', 'Jharkhand']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
622,funding_based,Visualize NCAP city-level funding for FY 2021-22 as a horizontal bar chart for the top 8 cities.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = ncap_funding_data[['city','state','Amount released during FY 2021-22']].copy()
    df.columns = ['city','state','Amount (Cr)']
    df = df.nlargest(8, 'Amount (Cr)')
    df['City_State'] = df['city'] + ', ' + df['state']
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Amount (Cr):Q', title='Amount Released (Cr)'),
        y=alt.Y('City_State:N', sort='-x', title='City, State'),
        color=alt.Color('state:N', title='State'),
        tooltip=['city:N','state:N', alt.Tooltip('Amount (Cr):Q', format='.1f')]
    ).properties(title='Top 8 Cities by NCAP Funding – FY 2021-22', width=500, height=400)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = ncap_funding_data[['city','state','Amount released during FY 2021-22']].copy()
     df.columns = ['city','state','Amount (Cr)']
     df = df.nlargest(8, 'Amount (Cr)')
     df['City_State'] = df['city'] + ', ' + df['state']
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Amount (Cr):Q', title='Amount Released (Cr)'),
         y=alt.Y('City_State:N', sort='-x', title='City, State'),
         color=alt.Color('state:N', title='State'),
         tooltip=['city:N','state:N', alt.Tooltip('Amount (Cr):Q', format='.1f')]
     ).properties(title='Top 8 Cities by NCAP Funding – FY 2021-22', width=500, height=400)
     return chart


</code>
"
623,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Himachal Pradesh, Punjab, and Tripura from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Himachal Pradesh', 'Punjab', 'Tripura']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Himachal Pradesh vs Punjab vs Tripura', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Himachal Pradesh', 'Punjab', 'Tripura']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Himachal Pradesh vs Punjab vs Tripura', width=550, height=320)
     return chart


</code>
"
624,spatio_temporal_aggregation,Create a grouped bar chart comparing the average PM2.5 in Winter vs Summer for the top 5 most polluted states.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    top6 = data.groupby('state')['PM2.5'].mean().nlargest(5).index.tolist()
    df = data[data['state'].isin(top6)].copy()
    df['Season'] = df['Timestamp'].dt.month.apply(
        lambda m: 'Winter' if m in [12,1,2] else ('Summer' if m in [3,4,5] else None))
    df = df.dropna(subset=['Season'])
    df = df.groupby(['state','Season'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('state:N', title='State'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('Season:N', title='Season'),
        xOffset='Season:N',
        tooltip=['state:N','Season:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Winter vs Summer PM2.5 – Top 5 Polluted States', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     top6 = data.groupby('state')['PM2.5'].mean().nlargest(5).index.tolist()
     df = data[data['state'].isin(top6)].copy()
     df['Season'] = df['Timestamp'].dt.month.apply(
         lambda m: 'Winter' if m in [12,1,2] else ('Summer' if m in [3,4,5] else None))
     df = df.dropna(subset=['Season'])
     df = df.groupby(['state','Season'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('state:N', title='State'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('Season:N', title='Season'),
         xOffset='Season:N',
         tooltip=['state:N','Season:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Winter vs Summer PM2.5 – Top 5 Polluted States', width=550, height=320)
     return chart


</code>
"
625,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Manipur stations in 2021, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Manipur') & (data['Timestamp'].dt.year == 2021)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Manipur Stations 2021', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Manipur') & (data['Timestamp'].dt.year == 2021)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Manipur Stations 2021', width=450, height=350)


</code>
"
626,temporal_aggregation,Show the monthly average PM10 trend for Jalandhar from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Jalandhar'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Jalandhar (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Jalandhar'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Jalandhar (2019–2024)', width=600, height=300)
     return chart


</code>
"
627,spatio_temporal_aggregation,Show a heatmap of average PM2.5 for the top 13 most polluted states by month for 2017.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    top8 = data.groupby('state')['PM2.5'].mean().nlargest(13).index.tolist()
    df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2017)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_rect().encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('state:N', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
        tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Heatmap – Top 13 Polluted States by Month (2017)', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     top8 = data.groupby('state')['PM2.5'].mean().nlargest(13).index.tolist()
     df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2017)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_rect().encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('state:N', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
         tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Heatmap – Top 13 Polluted States by Month (2017)', width=500, height=300)
     return chart


</code>
"
628,temporal_aggregation,Show the monthly average PM2.5 for Kalyan in 2017 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Kalyan') & (data['Timestamp'].dt.year == 2017)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Kalyan 2017', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Kalyan') & (data['Timestamp'].dt.year == 2017)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Kalyan 2017', width=450, height=280)


</code>
"
629,spatial_aggregation,Visualize the bottom 6 states with the lowest average PM2.5 in 2024 using a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2024]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nsmallest(6, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Bottom 6 States by Average PM2.5 in 2024', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2024]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nsmallest(6, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Bottom 6 States by Average PM2.5 in 2024', width=500, height=300)
     return chart


</code>
"
630,temporal_aggregation,Show the monthly average PM2.5 for Visakhapatnam in 2024 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Visakhapatnam') & (data['Timestamp'].dt.year == 2024)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Visakhapatnam 2024', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Visakhapatnam') & (data['Timestamp'].dt.year == 2024)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Visakhapatnam 2024', width=450, height=280)


</code>
"
631,temporal_aggregation,Plot the weekly average PM2.5 for Guwahati in 2020 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Guwahati') & (data['Timestamp'].dt.year == 2020)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Guwahati 2020', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Guwahati') & (data['Timestamp'].dt.year == 2020)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Guwahati 2020', width=600, height=300)
     return chart


</code>
"
632,spatial_aggregation,Plot the top 8 states by average PM2.5 in 2018 as a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2018]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nlargest(8, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 8 States by Average PM2.5 in 2018', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2018]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nlargest(8, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 8 States by Average PM2.5 in 2018', width=500, height=300)
     return chart


</code>
"
633,specific_pattern,Show a cumulative area chart of PM2.5 readings for Gummidipoondi across 2022.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Gummidipoondi') & (data['Timestamp'].dt.year == 2022)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Gummidipoondi 2022', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Gummidipoondi') & (data['Timestamp'].dt.year == 2022)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Gummidipoondi 2022', width=600, height=300)
     return chart


</code>
"
634,spatio_temporal_aggregation,Show the monthly average PM2.5 for Karnataka across each year from 2017 to 2024 as small multiples (faceted by year).,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Karnataka'].copy()
    df['Year'] = df['Timestamp'].dt.year
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='steelblue').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5'),
        tooltip=['Year:O','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).facet(
        facet=alt.Facet('Year:O', title='Year'),
        columns=4
    ).properties(title='Monthly PM2.5 in Karnataka by Year (2017–2024)')
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Karnataka'].copy()
     df['Year'] = df['Timestamp'].dt.year
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='steelblue').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5'),
         tooltip=['Year:O','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).facet(
         facet=alt.Facet('Year:O', title='Year'),
         columns=4
     ).properties(title='Monthly PM2.5 in Karnataka by Year (2017–2024)')
     return chart


</code>
"
635,spatio_temporal_aggregation,"Create a faceted bar chart showing top 10 states by average PM2.5 per year for 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year.isin([2020,2021,2022,2023])].copy()
    df['Year'] = df['Timestamp'].dt.year
    agg = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    top = agg.groupby('Year').apply(lambda x: x.nlargest(10,'PM2.5')).reset_index(drop=True)
    chart = alt.Chart(top).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Avg PM2.5'),
        y=alt.Y('state:N', sort='-x'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).facet(facet='Year:O', columns=2).properties(title='Top 10 States by PM2.5 per Year')
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year.isin([2020,2021,2022,2023])].copy()
     df['Year'] = df['Timestamp'].dt.year
     agg = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     top = agg.groupby('Year').apply(lambda x: x.nlargest(10,'PM2.5')).reset_index(drop=True)
     chart = alt.Chart(top).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Avg PM2.5'),
         y=alt.Y('state:N', sort='-x'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).facet(facet='Year:O', columns=2).properties(title='Top 10 States by PM2.5 per Year')
     return chart


</code>
"
636,temporal_aggregation,Plot the weekly average PM2.5 for Kota in 2019 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Kota') & (data['Timestamp'].dt.year == 2019)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Kota 2019', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Kota') & (data['Timestamp'].dt.year == 2019)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Kota 2019', width=600, height=300)
     return chart


</code>
"
637,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Mizoram stations in 2017, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Mizoram') & (data['Timestamp'].dt.year == 2017)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Mizoram Stations 2017', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Mizoram') & (data['Timestamp'].dt.year == 2017)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Mizoram Stations 2017', width=450, height=350)


</code>
"
638,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Rajasthan, Andhra Pradesh, and Chandigarh from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Rajasthan', 'Andhra Pradesh', 'Chandigarh']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Rajasthan vs Andhra Pradesh vs Chandigarh', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Rajasthan', 'Andhra Pradesh', 'Chandigarh']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Rajasthan vs Andhra Pradesh vs Chandigarh', width=550, height=320)
     return chart


</code>
"
639,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Darbhanga, Kishanganj, and Perundurai in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Darbhanga', 'Kishanganj', 'Perundurai']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Darbhanga vs Kishanganj vs Perundurai – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Darbhanga', 'Kishanganj', 'Perundurai']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Darbhanga vs Kishanganj vs Perundurai – 2022', width=550, height=320)
     return chart


</code>
"
640,temporal_aggregation,Show the monthly average PM2.5 for Hubballi in 2019 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Hubballi') & (data['Timestamp'].dt.year == 2019)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Hubballi 2019', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Hubballi') & (data['Timestamp'].dt.year == 2019)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Hubballi 2019', width=450, height=280)


</code>
"
641,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Odisha, Meghalaya, and West Bengal in 2021 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Odisha', 'Meghalaya', 'West Bengal']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Odisha, Meghalaya, UP – 2021', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Odisha', 'Meghalaya', 'West Bengal']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Odisha, Meghalaya, UP – 2021', width=550, height=320)
     return chart


</code>
"
642,specific_pattern,Plot the rolling 30-day average PM2.5 for Haryana in 2019 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Haryana') & (data['Timestamp'].dt.year == 2019)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Haryana 2019', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Haryana') & (data['Timestamp'].dt.year == 2019)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Haryana 2019', width=600, height=300)


</code>
"
643,temporal_aggregation,Show a monthly bar chart of the number of days Tamil Nadu exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2023.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Tamil Nadu') & (data['Timestamp'].dt.year == 2023)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Tamil Nadu Exceeded WHO PM2.5 Guideline per Month – 2023', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Tamil Nadu') & (data['Timestamp'].dt.year == 2023)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Tamil Nadu Exceeded WHO PM2.5 Guideline per Month – 2023', width=500, height=300)
     return chart


</code>
"
644,temporal_aggregation,Show a monthly bar chart of the number of days Rajasthan exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2018.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Rajasthan') & (data['Timestamp'].dt.year == 2018)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Rajasthan Exceeded WHO PM2.5 Guideline per Month – 2018', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Rajasthan') & (data['Timestamp'].dt.year == 2018)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Rajasthan Exceeded WHO PM2.5 Guideline per Month – 2018', width=500, height=300)
     return chart


</code>
"
645,spatial_aggregation,Show a bar chart of the top 15 cities by median PM2.5 in 2024.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2024]
    df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
    df = df.nlargest(15, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
        y=alt.Y('city:N', sort='-x', title='City'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
        tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 15 Cities by Median PM2.5 in 2024', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2024]
     df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
     df = df.nlargest(15, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
         y=alt.Y('city:N', sort='-x', title='City'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
         tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 15 Cities by Median PM2.5 in 2024', width=500, height=300)
     return chart


</code>
"
646,spatial_aggregation,Plot the top 7 states by average PM2.5 in 2021 as a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2021]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nlargest(7, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 7 States by Average PM2.5 in 2021', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2021]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nlargest(7, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 7 States by Average PM2.5 in 2021', width=500, height=300)
     return chart


</code>
"
647,spatial_aggregation,Plot the distribution of PM2.5 values in Gujarat across all years using a histogram.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Gujarat'].dropna(subset=['PM2.5'])
    df = df[['PM2.5']]
    chart = alt.Chart(df).mark_bar(color='steelblue', opacity=0.75).encode(
        x=alt.X('PM2\.5:Q', bin=alt.Bin(maxbins=40), title='PM2.5 (µg/m³)'),
        y=alt.Y('count()', title='Number of Observations'),
        tooltip=[alt.Tooltip('PM2\.5:Q', bin=True), 'count()']
    ).properties(title='PM2.5 Distribution – Gujarat (All Years)', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Gujarat'].dropna(subset=['PM2.5'])
     df = df[['PM2.5']]
     chart = alt.Chart(df).mark_bar(color='steelblue', opacity=0.75).encode(
         x=alt.X('PM2\.5:Q', bin=alt.Bin(maxbins=40), title='PM2.5 (µg/m³)'),
         y=alt.Y('count()', title='Number of Observations'),
         tooltip=[alt.Tooltip('PM2\.5:Q', bin=True), 'count()']
     ).properties(title='PM2.5 Distribution – Gujarat (All Years)', width=500, height=300)
     return chart


</code>
"
648,spatial_aggregation,Show a bar chart of the top 7 cities by median PM2.5 in 2023.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2023]
    df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
    df = df.nlargest(7, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
        y=alt.Y('city:N', sort='-x', title='City'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
        tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 7 Cities by Median PM2.5 in 2023', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2023]
     df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
     df = df.nlargest(7, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
         y=alt.Y('city:N', sort='-x', title='City'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
         tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 7 Cities by Median PM2.5 in 2023', width=500, height=300)
     return chart


</code>
"
649,spatial_aggregation,Plot the top 13 states by average PM2.5 in 2018 as a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2018]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nlargest(13, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 13 States by Average PM2.5 in 2018', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2018]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nlargest(13, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 13 States by Average PM2.5 in 2018', width=500, height=300)
     return chart


</code>
"
650,spatial_aggregation,"Show the top 13 states by average PM10 in 2017 as a bar chart, sorted in descending order.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2017]
    df = df.groupby('state')['PM10'].mean().reset_index().dropna()
    df = df.nlargest(13, 'PM10')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Top 13 States by Average PM10 in 2017', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2017]
     df = df.groupby('state')['PM10'].mean().reset_index().dropna()
     df = df.nlargest(13, 'PM10')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Top 13 States by Average PM10 in 2017', width=500, height=300)
     return chart


</code>
"
651,spatial_aggregation,Plot the top 13 states by average PM2.5 in 2017 as a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2017]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nlargest(13, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 13 States by Average PM2.5 in 2017', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2017]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nlargest(13, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 13 States by Average PM2.5 in 2017', width=500, height=300)
     return chart


</code>
"
652,spatio_temporal_aggregation,Show the monthly average PM2.5 for Andhra Pradesh across each year from 2017 to 2024 as small multiples (faceted by year).,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Andhra Pradesh'].copy()
    df['Year'] = df['Timestamp'].dt.year
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='steelblue').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5'),
        tooltip=['Year:O','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).facet(
        facet=alt.Facet('Year:O', title='Year'),
        columns=4
    ).properties(title='Monthly PM2.5 in Andhra Pradesh by Year (2017–2024)')
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Andhra Pradesh'].copy()
     df['Year'] = df['Timestamp'].dt.year
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='steelblue').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5'),
         tooltip=['Year:O','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).facet(
         facet=alt.Facet('Year:O', title='Year'),
         columns=4
     ).properties(title='Monthly PM2.5 in Andhra Pradesh by Year (2017–2024)')
     return chart


</code>
"
653,specific_pattern,Show a cumulative area chart of PM2.5 readings for Pune across 2023.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Pune') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Pune 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Pune') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Pune 2023', width=600, height=300)
     return chart


</code>
"
654,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Manipur, Nagaland, and Haryana in 2018 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Manipur', 'Nagaland', 'Haryana']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2018)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Manipur, Nagaland, UP – 2018', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Manipur', 'Nagaland', 'Haryana']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2018)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Manipur, Nagaland, UP – 2018', width=550, height=320)
     return chart


</code>
"
655,spatial_aggregation,Visualize the bottom 11 states with the lowest average PM2.5 in 2017 using a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2017]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nsmallest(11, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Bottom 11 States by Average PM2.5 in 2017', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2017]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nsmallest(11, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Bottom 11 States by Average PM2.5 in 2017', width=500, height=300)
     return chart


</code>
"
656,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Mizoram, Bihar, and Tripura across 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Mizoram', 'Bihar', 'Tripura']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Mizoram', 'Bihar', 'Tripura']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
     return chart


</code>
"
657,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Delhi, Tripura, and Uttar Pradesh from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Delhi', 'Tripura', 'Uttar Pradesh']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Delhi vs Tripura vs Uttar Pradesh', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Delhi', 'Tripura', 'Uttar Pradesh']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Delhi vs Tripura vs Uttar Pradesh', width=550, height=320)
     return chart


</code>
"
658,spatial_aggregation,Show a bar chart of the top 9 cities by median PM2.5 in 2023.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2023]
    df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
    df = df.nlargest(9, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
        y=alt.Y('city:N', sort='-x', title='City'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
        tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 9 Cities by Median PM2.5 in 2023', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2023]
     df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
     df = df.nlargest(9, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
         y=alt.Y('city:N', sort='-x', title='City'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
         tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 9 Cities by Median PM2.5 in 2023', width=500, height=300)
     return chart


</code>
"
659,spatial_aggregation,Visualize the bottom 10 states with the lowest average PM2.5 in 2018 using a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2018]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nsmallest(10, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Bottom 10 States by Average PM2.5 in 2018', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2018]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nsmallest(10, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Bottom 10 States by Average PM2.5 in 2018', width=500, height=300)
     return chart


</code>
"
660,spatio_temporal_aggregation,"Create a faceted bar chart showing top 14 states by average PM2.5 per year for 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year.isin([2019,2020,2021,2022])].copy()
    df['Year'] = df['Timestamp'].dt.year
    agg = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    top = agg.groupby('Year').apply(lambda x: x.nlargest(14,'PM2.5')).reset_index(drop=True)
    chart = alt.Chart(top).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Avg PM2.5'),
        y=alt.Y('state:N', sort='-x'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).facet(facet='Year:O', columns=2).properties(title='Top 14 States by PM2.5 per Year')
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year.isin([2019,2020,2021,2022])].copy()
     df['Year'] = df['Timestamp'].dt.year
     agg = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     top = agg.groupby('Year').apply(lambda x: x.nlargest(14,'PM2.5')).reset_index(drop=True)
     chart = alt.Chart(top).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Avg PM2.5'),
         y=alt.Y('state:N', sort='-x'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).facet(facet='Year:O', columns=2).properties(title='Top 14 States by PM2.5 per Year')
     return chart


</code>
"
661,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Tripura, Telangana, and Karnataka in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Tripura', 'Telangana', 'Karnataka']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Tripura, Telangana, UP – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Tripura', 'Telangana', 'Karnataka']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Tripura, Telangana, UP – 2022', width=550, height=320)
     return chart


</code>
"
662,specific_pattern,Plot the rolling 30-day average PM2.5 for Jammu and Kashmir in 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Jammu and Kashmir') & (data['Timestamp'].dt.year == 2022)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Jammu and Kashmir 2022', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Jammu and Kashmir') & (data['Timestamp'].dt.year == 2022)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Jammu and Kashmir 2022', width=600, height=300)


</code>
"
663,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Telangana, Jammu and Kashmir, and Assam across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Telangana', 'Jammu and Kashmir', 'Assam']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Telangana', 'Jammu and Kashmir', 'Assam']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
664,population_based,Bar chart of PM2.5 per capita (average PM2.5 × 1000 / population) for each state in 2024.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    pm = data[data['Timestamp'].dt.year == 2024].groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = pm.merge(states_data[['state','population']], on='state')
    df['PM2.5 per Capita (×1000)'] = df['PM2.5'] / df['population'] * 1e6
    df = df.sort_values('PM2.5 per Capita (×1000)', ascending=False)
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5 per Capita (×1000):Q', title='PM2.5 per Million Population'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM2\.5 per Capita (×1000):Q', scale=alt.Scale(scheme='purples'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5 per Capita (×1000):Q', format='.4f')]
    ).properties(title='PM2.5 Per-Capita Pollution Index by State – 2024', width=500, height=400)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     pm = data[data['Timestamp'].dt.year == 2024].groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = pm.merge(states_data[['state','population']], on='state')
     df['PM2.5 per Capita (×1000)'] = df['PM2.5'] / df['population'] * 1e6
     df = df.sort_values('PM2.5 per Capita (×1000)', ascending=False)
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5 per Capita (×1000):Q', title='PM2.5 per Million Population'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM2\.5 per Capita (×1000):Q', scale=alt.Scale(scheme='purples'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5 per Capita (×1000):Q', format='.4f')]
     ).properties(title='PM2.5 Per-Capita Pollution Index by State – 2024', width=500, height=400)
     return chart


</code>
"
665,spatio_temporal_aggregation,Create a heatmap showing the average PM2.5 by month (x-axis) and year (y-axis) for Andhra Pradesh.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Andhra Pradesh'].copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_rect().encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Year:N', title='Year'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='Avg PM2.5'),
        tooltip=['Year:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Heatmap – Andhra Pradesh (Month × Year)', width=500, height=280)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Andhra Pradesh'].copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_rect().encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Year:N', title='Year'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='Avg PM2.5'),
         tooltip=['Year:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Heatmap – Andhra Pradesh (Month × Year)', width=500, height=280)
     return chart


</code>
"
666,spatial_aggregation,Plot the distribution of PM2.5 values in Haryana across all years using a histogram.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Haryana'].dropna(subset=['PM2.5'])
    df = df[['PM2.5']]
    chart = alt.Chart(df).mark_bar(color='steelblue', opacity=0.75).encode(
        x=alt.X('PM2\.5:Q', bin=alt.Bin(maxbins=40), title='PM2.5 (µg/m³)'),
        y=alt.Y('count()', title='Number of Observations'),
        tooltip=[alt.Tooltip('PM2\.5:Q', bin=True), 'count()']
    ).properties(title='PM2.5 Distribution – Haryana (All Years)', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Haryana'].dropna(subset=['PM2.5'])
     df = df[['PM2.5']]
     chart = alt.Chart(df).mark_bar(color='steelblue', opacity=0.75).encode(
         x=alt.X('PM2\.5:Q', bin=alt.Bin(maxbins=40), title='PM2.5 (µg/m³)'),
         y=alt.Y('count()', title='Number of Observations'),
         tooltip=[alt.Tooltip('PM2\.5:Q', bin=True), 'count()']
     ).properties(title='PM2.5 Distribution – Haryana (All Years)', width=500, height=300)
     return chart


</code>
"
667,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Jharkhand, West Bengal, and Meghalaya from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Jharkhand', 'West Bengal', 'Meghalaya']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Jharkhand vs West Bengal vs Meghalaya', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Jharkhand', 'West Bengal', 'Meghalaya']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Jharkhand vs West Bengal vs Meghalaya', width=550, height=320)
     return chart


</code>
"
668,specific_pattern,Plot the rolling 30-day average PM2.5 for Puducherry in 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Puducherry') & (data['Timestamp'].dt.year == 2022)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Puducherry 2022', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Puducherry') & (data['Timestamp'].dt.year == 2022)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Puducherry 2022', width=600, height=300)


</code>
"
669,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Haryana, Assam, and Chandigarh in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Haryana', 'Assam', 'Chandigarh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Haryana, Assam, UP – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Haryana', 'Assam', 'Chandigarh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Haryana, Assam, UP – 2023', width=550, height=320)
     return chart


</code>
"
670,spatial_aggregation,Plot the distribution of PM2.5 values in Chandigarh across all years using a histogram.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Chandigarh'].dropna(subset=['PM2.5'])
    df = df[['PM2.5']]
    chart = alt.Chart(df).mark_bar(color='steelblue', opacity=0.75).encode(
        x=alt.X('PM2\.5:Q', bin=alt.Bin(maxbins=40), title='PM2.5 (µg/m³)'),
        y=alt.Y('count()', title='Number of Observations'),
        tooltip=[alt.Tooltip('PM2\.5:Q', bin=True), 'count()']
    ).properties(title='PM2.5 Distribution – Chandigarh (All Years)', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Chandigarh'].dropna(subset=['PM2.5'])
     df = df[['PM2.5']]
     chart = alt.Chart(df).mark_bar(color='steelblue', opacity=0.75).encode(
         x=alt.X('PM2\.5:Q', bin=alt.Bin(maxbins=40), title='PM2.5 (µg/m³)'),
         y=alt.Y('count()', title='Number of Observations'),
         tooltip=[alt.Tooltip('PM2\.5:Q', bin=True), 'count()']
     ).properties(title='PM2.5 Distribution – Chandigarh (All Years)', width=500, height=300)
     return chart


</code>
"
671,spatio_temporal_aggregation,Show a heatmap of average PM2.5 for the top 6 most polluted states by month for 2017.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    top8 = data.groupby('state')['PM2.5'].mean().nlargest(6).index.tolist()
    df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2017)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_rect().encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('state:N', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
        tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Heatmap – Top 6 Polluted States by Month (2017)', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     top8 = data.groupby('state')['PM2.5'].mean().nlargest(6).index.tolist()
     df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2017)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_rect().encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('state:N', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
         tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Heatmap – Top 6 Polluted States by Month (2017)', width=500, height=300)
     return chart


</code>
"
672,spatial_aggregation,Plot the top 12 states by average PM2.5 in 2019 as a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2019]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nlargest(12, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 12 States by Average PM2.5 in 2019', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2019]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nlargest(12, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 12 States by Average PM2.5 in 2019', width=500, height=300)
     return chart


</code>
"
673,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Meghalaya, Kerala, and Manipur across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Meghalaya', 'Kerala', 'Manipur']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Meghalaya', 'Kerala', 'Manipur']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
674,temporal_aggregation,Show a monthly bar chart of the number of days Karnataka exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2023.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Karnataka') & (data['Timestamp'].dt.year == 2023)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Karnataka Exceeded WHO PM2.5 Guideline per Month – 2023', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Karnataka') & (data['Timestamp'].dt.year == 2023)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Karnataka Exceeded WHO PM2.5 Guideline per Month – 2023', width=500, height=300)
     return chart


</code>
"
675,temporal_aggregation,Show a monthly bar chart of the number of days Haryana exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2018.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Haryana') & (data['Timestamp'].dt.year == 2018)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Haryana Exceeded WHO PM2.5 Guideline per Month – 2018', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Haryana') & (data['Timestamp'].dt.year == 2018)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Haryana Exceeded WHO PM2.5 Guideline per Month – 2018', width=500, height=300)
     return chart


</code>
"
676,spatial_aggregation,Show a bar chart of the top 11 cities by median PM2.5 in 2018.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2018]
    df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
    df = df.nlargest(11, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
        y=alt.Y('city:N', sort='-x', title='City'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
        tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 11 Cities by Median PM2.5 in 2018', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2018]
     df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
     df = df.nlargest(11, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
         y=alt.Y('city:N', sort='-x', title='City'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
         tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 11 Cities by Median PM2.5 in 2018', width=500, height=300)
     return chart


</code>
"
677,spatio_temporal_aggregation,"Create a faceted bar chart showing top 6 states by average PM2.5 per year for 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year.isin([2021,2022,2023,2024])].copy()
    df['Year'] = df['Timestamp'].dt.year
    agg = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    top = agg.groupby('Year').apply(lambda x: x.nlargest(6,'PM2.5')).reset_index(drop=True)
    chart = alt.Chart(top).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Avg PM2.5'),
        y=alt.Y('state:N', sort='-x'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).facet(facet='Year:O', columns=2).properties(title='Top 6 States by PM2.5 per Year')
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year.isin([2021,2022,2023,2024])].copy()
     df['Year'] = df['Timestamp'].dt.year
     agg = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     top = agg.groupby('Year').apply(lambda x: x.nlargest(6,'PM2.5')).reset_index(drop=True)
     chart = alt.Chart(top).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Avg PM2.5'),
         y=alt.Y('state:N', sort='-x'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).facet(facet='Year:O', columns=2).properties(title='Top 6 States by PM2.5 per Year')
     return chart


</code>
"
678,spatio_temporal_aggregation,Show a heatmap of average PM2.5 for the top 8 most polluted states by month for 2019.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    top8 = data.groupby('state')['PM2.5'].mean().nlargest(8).index.tolist()
    df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_rect().encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('state:N', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
        tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Heatmap – Top 8 Polluted States by Month (2019)', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     top8 = data.groupby('state')['PM2.5'].mean().nlargest(8).index.tolist()
     df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_rect().encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('state:N', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
         tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Heatmap – Top 8 Polluted States by Month (2019)', width=500, height=300)
     return chart


</code>
"
679,spatial_aggregation,Plot the distribution of PM2.5 values in Maharashtra across all years using a histogram.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Maharashtra'].dropna(subset=['PM2.5'])
    df = df[['PM2.5']]
    chart = alt.Chart(df).mark_bar(color='steelblue', opacity=0.75).encode(
        x=alt.X('PM2\.5:Q', bin=alt.Bin(maxbins=40), title='PM2.5 (µg/m³)'),
        y=alt.Y('count()', title='Number of Observations'),
        tooltip=[alt.Tooltip('PM2\.5:Q', bin=True), 'count()']
    ).properties(title='PM2.5 Distribution – Maharashtra (All Years)', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Maharashtra'].dropna(subset=['PM2.5'])
     df = df[['PM2.5']]
     chart = alt.Chart(df).mark_bar(color='steelblue', opacity=0.75).encode(
         x=alt.X('PM2\.5:Q', bin=alt.Bin(maxbins=40), title='PM2.5 (µg/m³)'),
         y=alt.Y('count()', title='Number of Observations'),
         tooltip=[alt.Tooltip('PM2\.5:Q', bin=True), 'count()']
     ).properties(title='PM2.5 Distribution – Maharashtra (All Years)', width=500, height=300)
     return chart


</code>
"
680,temporal_aggregation,Show the monthly average PM2.5 for Boisar in 2020 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Boisar') & (data['Timestamp'].dt.year == 2020)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Boisar 2020', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Boisar') & (data['Timestamp'].dt.year == 2020)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Boisar 2020', width=450, height=280)


</code>
"
681,temporal_aggregation,Show the monthly average PM10 trend for Ahmednagar from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Ahmednagar'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Ahmednagar (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Ahmednagar'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Ahmednagar (2019–2024)', width=600, height=300)
     return chart


</code>
"
682,spatio_temporal_aggregation,Plot a heatmap of average PM10 by state (y-axis) and month (x-axis) for 2020.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2020].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['state','Month'])['PM10'].mean().reset_index().dropna()
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_rect().encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('state:N', title='State'),
        color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), title='Avg PM10'),
        tooltip=['state:N','MonthName:N', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='PM10 Heatmap by State and Month – 2020', width=500, height=400)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2020].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['state','Month'])['PM10'].mean().reset_index().dropna()
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_rect().encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('state:N', title='State'),
         color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), title='Avg PM10'),
         tooltip=['state:N','MonthName:N', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='PM10 Heatmap by State and Month – 2020', width=500, height=400)
     return chart


</code>
"
683,spatio_temporal_aggregation,"Create a faceted bar chart showing top 14 states by average PM2.5 per year for 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year.isin([2020,2021,2022,2023])].copy()
    df['Year'] = df['Timestamp'].dt.year
    agg = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    top = agg.groupby('Year').apply(lambda x: x.nlargest(14,'PM2.5')).reset_index(drop=True)
    chart = alt.Chart(top).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Avg PM2.5'),
        y=alt.Y('state:N', sort='-x'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).facet(facet='Year:O', columns=2).properties(title='Top 14 States by PM2.5 per Year')
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year.isin([2020,2021,2022,2023])].copy()
     df['Year'] = df['Timestamp'].dt.year
     agg = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     top = agg.groupby('Year').apply(lambda x: x.nlargest(14,'PM2.5')).reset_index(drop=True)
     chart = alt.Chart(top).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Avg PM2.5'),
         y=alt.Y('state:N', sort='-x'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).facet(facet='Year:O', columns=2).properties(title='Top 14 States by PM2.5 per Year')
     return chart


</code>
"
684,spatio_temporal_aggregation,Show the monthly average PM2.5 for Rajasthan across each year from 2017 to 2024 as small multiples (faceted by year).,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Rajasthan'].copy()
    df['Year'] = df['Timestamp'].dt.year
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='steelblue').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5'),
        tooltip=['Year:O','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).facet(
        facet=alt.Facet('Year:O', title='Year'),
        columns=4
    ).properties(title='Monthly PM2.5 in Rajasthan by Year (2017–2024)')
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Rajasthan'].copy()
     df['Year'] = df['Timestamp'].dt.year
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='steelblue').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5'),
         tooltip=['Year:O','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).facet(
         facet=alt.Facet('Year:O', title='Year'),
         columns=4
     ).properties(title='Monthly PM2.5 in Rajasthan by Year (2017–2024)')
     return chart


</code>
"
685,specific_pattern,Show a cumulative area chart of PM2.5 readings for Sirsa across 2023.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Sirsa') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Sirsa 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Sirsa') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Sirsa 2023', width=600, height=300)
     return chart


</code>
"
686,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Haryana, Karnataka, and Telangana in 2019 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Haryana', 'Karnataka', 'Telangana']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Haryana, Karnataka, UP – 2019', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Haryana', 'Karnataka', 'Telangana']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Haryana, Karnataka, UP – 2019', width=550, height=320)
     return chart


</code>
"
687,spatial_aggregation,"Show the top 12 states by average PM10 in 2023 as a bar chart, sorted in descending order.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2023]
    df = df.groupby('state')['PM10'].mean().reset_index().dropna()
    df = df.nlargest(12, 'PM10')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Top 12 States by Average PM10 in 2023', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2023]
     df = df.groupby('state')['PM10'].mean().reset_index().dropna()
     df = df.nlargest(12, 'PM10')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Top 12 States by Average PM10 in 2023', width=500, height=300)
     return chart


</code>
"
688,temporal_aggregation,Plot the weekly average PM2.5 for Khanna in 2020 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Khanna') & (data['Timestamp'].dt.year == 2020)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Khanna 2020', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Khanna') & (data['Timestamp'].dt.year == 2020)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Khanna 2020', width=600, height=300)
     return chart


</code>
"
689,specific_pattern,Plot the rolling 30-day average PM2.5 for Chandigarh in 2020 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Chandigarh') & (data['Timestamp'].dt.year == 2020)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Chandigarh 2020', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Chandigarh') & (data['Timestamp'].dt.year == 2020)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Chandigarh 2020', width=600, height=300)


</code>
"
690,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Chandigarh, Puducherry, and Punjab in 2019 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Chandigarh', 'Puducherry', 'Punjab']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Chandigarh, Puducherry, UP – 2019', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Chandigarh', 'Puducherry', 'Punjab']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Chandigarh, Puducherry, UP – 2019', width=550, height=320)
     return chart


</code>
"
691,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Assam, Nagaland, and Madhya Pradesh across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Assam', 'Nagaland', 'Madhya Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Assam', 'Nagaland', 'Madhya Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
692,specific_pattern,Show a cumulative area chart of PM2.5 readings for Vapi across 2021.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Vapi') & (data['Timestamp'].dt.year == 2021)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Vapi 2021', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Vapi') & (data['Timestamp'].dt.year == 2021)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Vapi 2021', width=600, height=300)
     return chart


</code>
"
693,specific_pattern,Show a cumulative area chart of PM2.5 readings for Udaipur across 2024.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Udaipur') & (data['Timestamp'].dt.year == 2024)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Udaipur 2024', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Udaipur') & (data['Timestamp'].dt.year == 2024)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Udaipur 2024', width=600, height=300)
     return chart


</code>
"
694,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Jharkhand, Puducherry, and Chhattisgarh in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Jharkhand', 'Puducherry', 'Chhattisgarh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Jharkhand, Puducherry, UP – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Jharkhand', 'Puducherry', 'Chhattisgarh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Jharkhand, Puducherry, UP – 2023', width=550, height=320)
     return chart


</code>
"
695,specific_pattern,Plot the rolling 30-day average PM2.5 for Karnataka in 2023 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Karnataka') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Karnataka 2023', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Karnataka') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Karnataka 2023', width=600, height=300)


</code>
"
696,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Andhra Pradesh, Haryana, and Bihar across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Andhra Pradesh', 'Haryana', 'Bihar']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Andhra Pradesh', 'Haryana', 'Bihar']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
697,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Manipur, Meghalaya, and Rajasthan from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Manipur', 'Meghalaya', 'Rajasthan']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Manipur vs Meghalaya vs Rajasthan', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Manipur', 'Meghalaya', 'Rajasthan']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Manipur vs Meghalaya vs Rajasthan', width=550, height=320)
     return chart


</code>
"
698,specific_pattern,Plot the rolling 30-day average PM2.5 for Punjab in 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Punjab') & (data['Timestamp'].dt.year == 2022)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Punjab 2022', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Punjab') & (data['Timestamp'].dt.year == 2022)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Punjab 2022', width=600, height=300)


</code>
"
699,spatial_aggregation,"Show the top 6 states by average PM10 in 2021 as a bar chart, sorted in descending order.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2021]
    df = df.groupby('state')['PM10'].mean().reset_index().dropna()
    df = df.nlargest(6, 'PM10')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Top 6 States by Average PM10 in 2021', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2021]
     df = df.groupby('state')['PM10'].mean().reset_index().dropna()
     df = df.nlargest(6, 'PM10')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Top 6 States by Average PM10 in 2021', width=500, height=300)
     return chart


</code>
"
700,spatio_temporal_aggregation,Create a heatmap showing the average PM2.5 by month (x-axis) and year (y-axis) for Assam.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Assam'].copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_rect().encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Year:N', title='Year'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='Avg PM2.5'),
        tooltip=['Year:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Heatmap – Assam (Month × Year)', width=500, height=280)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Assam'].copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_rect().encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Year:N', title='Year'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='Avg PM2.5'),
         tooltip=['Year:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Heatmap – Assam (Month × Year)', width=500, height=280)
     return chart


</code>
"
701,spatio_temporal_aggregation,"Create a faceted bar chart showing top 6 states by average PM2.5 per year for 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year.isin([2017,2018,2019,2020])].copy()
    df['Year'] = df['Timestamp'].dt.year
    agg = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    top = agg.groupby('Year').apply(lambda x: x.nlargest(6,'PM2.5')).reset_index(drop=True)
    chart = alt.Chart(top).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Avg PM2.5'),
        y=alt.Y('state:N', sort='-x'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).facet(facet='Year:O', columns=2).properties(title='Top 6 States by PM2.5 per Year')
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year.isin([2017,2018,2019,2020])].copy()
     df['Year'] = df['Timestamp'].dt.year
     agg = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     top = agg.groupby('Year').apply(lambda x: x.nlargest(6,'PM2.5')).reset_index(drop=True)
     chart = alt.Chart(top).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Avg PM2.5'),
         y=alt.Y('state:N', sort='-x'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).facet(facet='Year:O', columns=2).properties(title='Top 6 States by PM2.5 per Year')
     return chart


</code>
"
702,spatial_aggregation,"Show the top 10 states by average PM10 in 2024 as a bar chart, sorted in descending order.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2024]
    df = df.groupby('state')['PM10'].mean().reset_index().dropna()
    df = df.nlargest(10, 'PM10')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Top 10 States by Average PM10 in 2024', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2024]
     df = df.groupby('state')['PM10'].mean().reset_index().dropna()
     df = df.nlargest(10, 'PM10')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Top 10 States by Average PM10 in 2024', width=500, height=300)
     return chart


</code>
"
703,temporal_aggregation,Show a monthly bar chart of the number of days Gujarat exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2023.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Gujarat') & (data['Timestamp'].dt.year == 2023)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Gujarat Exceeded WHO PM2.5 Guideline per Month – 2023', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Gujarat') & (data['Timestamp'].dt.year == 2023)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Gujarat Exceeded WHO PM2.5 Guideline per Month – 2023', width=500, height=300)
     return chart


</code>
"
704,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Gujarat, Karnataka, and Tamil Nadu across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Gujarat', 'Karnataka', 'Tamil Nadu']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Gujarat', 'Karnataka', 'Tamil Nadu']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
705,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Andhra Pradesh, Nagaland, and Rajasthan across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Andhra Pradesh', 'Nagaland', 'Rajasthan']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Andhra Pradesh', 'Nagaland', 'Rajasthan']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
706,temporal_aggregation,Show the monthly average PM2.5 for Pudukottai in 2020 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Pudukottai') & (data['Timestamp'].dt.year == 2020)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Pudukottai 2020', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Pudukottai') & (data['Timestamp'].dt.year == 2020)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Pudukottai 2020', width=450, height=280)


</code>
"
707,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Kota, Tirupur, and Sirohi in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Kota', 'Tirupur', 'Sirohi']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Kota vs Tirupur vs Sirohi – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Kota', 'Tirupur', 'Sirohi']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Kota vs Tirupur vs Sirohi – 2023', width=550, height=320)
     return chart


</code>
"
708,temporal_aggregation,Show the monthly average PM10 trend for Byrnihat from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Byrnihat'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Byrnihat (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Byrnihat'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Byrnihat (2019–2024)', width=600, height=300)
     return chart


</code>
"
709,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Chhattisgarh, Bihar, and Jharkhand in 2019 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Chhattisgarh', 'Bihar', 'Jharkhand']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Chhattisgarh, Bihar, UP – 2019', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Chhattisgarh', 'Bihar', 'Jharkhand']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Chhattisgarh, Bihar, UP – 2019', width=550, height=320)
     return chart


</code>
"
710,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Meghalaya, Meghalaya, and Odisha across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Meghalaya', 'Meghalaya', 'Odisha']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Meghalaya', 'Meghalaya', 'Odisha']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
711,specific_pattern,Show a cumulative area chart of PM2.5 readings for Dhule across 2023.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Dhule') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Dhule 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Dhule') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Dhule 2023', width=600, height=300)
     return chart


</code>
"
712,temporal_aggregation,Show the monthly average PM10 trend for Kozhikode from 2017 to 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Kozhikode'].copy()
    df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Kozhikode (2017–2022)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Kozhikode'].copy()
     df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Kozhikode (2017–2022)', width=600, height=300)
     return chart


</code>
"
713,temporal_aggregation,Show the monthly average PM2.5 for Nandesari in 2024 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Nandesari') & (data['Timestamp'].dt.year == 2024)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Nandesari 2024', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Nandesari') & (data['Timestamp'].dt.year == 2024)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Nandesari 2024', width=450, height=280)


</code>
"
714,specific_pattern,Plot the rolling 30-day average PM2.5 for Kerala in 2020 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Kerala') & (data['Timestamp'].dt.year == 2020)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Kerala 2020', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Kerala') & (data['Timestamp'].dt.year == 2020)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Kerala 2020', width=600, height=300)


</code>
"
715,spatial_aggregation,"Show the top 8 states by average PM10 in 2024 as a bar chart, sorted in descending order.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2024]
    df = df.groupby('state')['PM10'].mean().reset_index().dropna()
    df = df.nlargest(8, 'PM10')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Top 8 States by Average PM10 in 2024', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2024]
     df = df.groupby('state')['PM10'].mean().reset_index().dropna()
     df = df.nlargest(8, 'PM10')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Top 8 States by Average PM10 in 2024', width=500, height=300)
     return chart


</code>
"
716,spatio_temporal_aggregation,Show a heatmap of average PM2.5 for the top 5 most polluted states by month for 2022.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    top8 = data.groupby('state')['PM2.5'].mean().nlargest(5).index.tolist()
    df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_rect().encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('state:N', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
        tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Heatmap – Top 5 Polluted States by Month (2022)', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     top8 = data.groupby('state')['PM2.5'].mean().nlargest(5).index.tolist()
     df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_rect().encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('state:N', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
         tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Heatmap – Top 5 Polluted States by Month (2022)', width=500, height=300)
     return chart


</code>
"
717,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Rajasthan, Odisha, and Telangana across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Rajasthan', 'Odisha', 'Telangana']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Rajasthan', 'Odisha', 'Telangana']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
718,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Arunachal Pradesh, Tripura, and Madhya Pradesh in 2019 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Arunachal Pradesh', 'Tripura', 'Madhya Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Arunachal Pradesh, Tripura, UP – 2019', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Arunachal Pradesh', 'Tripura', 'Madhya Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Arunachal Pradesh, Tripura, UP – 2019', width=550, height=320)
     return chart


</code>
"
719,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Byrnihat, Rairangpur, and Bhiwadi in 2019 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Byrnihat', 'Rairangpur', 'Bhiwadi']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Byrnihat vs Rairangpur vs Bhiwadi – 2019', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Byrnihat', 'Rairangpur', 'Bhiwadi']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Byrnihat vs Rairangpur vs Bhiwadi – 2019', width=550, height=320)
     return chart


</code>
"
720,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Rajasthan, Tamil Nadu, and Mizoram in 2018 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Rajasthan', 'Tamil Nadu', 'Mizoram']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2018)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Rajasthan, Tamil Nadu, UP – 2018', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Rajasthan', 'Tamil Nadu', 'Mizoram']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2018)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Rajasthan, Tamil Nadu, UP – 2018', width=550, height=320)
     return chart


</code>
"
721,temporal_aggregation,Show a monthly bar chart of the number of days Chandigarh exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2022.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Chandigarh') & (data['Timestamp'].dt.year == 2022)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Chandigarh Exceeded WHO PM2.5 Guideline per Month – 2022', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Chandigarh') & (data['Timestamp'].dt.year == 2022)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Chandigarh Exceeded WHO PM2.5 Guideline per Month – 2022', width=500, height=300)
     return chart


</code>
"
722,temporal_aggregation,Plot the weekly average PM2.5 for Gurugram in 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Gurugram') & (data['Timestamp'].dt.year == 2024)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Gurugram 2024', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Gurugram') & (data['Timestamp'].dt.year == 2024)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Gurugram 2024', width=600, height=300)
     return chart


</code>
"
723,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Haryana stations in 2017, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Haryana') & (data['Timestamp'].dt.year == 2017)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Haryana Stations 2017', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Haryana') & (data['Timestamp'].dt.year == 2017)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Haryana Stations 2017', width=450, height=350)


</code>
"
724,spatio_temporal_aggregation,Create a heatmap showing the average PM2.5 by month (x-axis) and year (y-axis) for Telangana.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Telangana'].copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_rect().encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Year:N', title='Year'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='Avg PM2.5'),
        tooltip=['Year:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Heatmap – Telangana (Month × Year)', width=500, height=280)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Telangana'].copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_rect().encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Year:N', title='Year'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='Avg PM2.5'),
         tooltip=['Year:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Heatmap – Telangana (Month × Year)', width=500, height=280)
     return chart


</code>
"
725,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Puducherry, Telangana, and Karnataka in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Puducherry', 'Telangana', 'Karnataka']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Puducherry, Telangana, UP – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Puducherry', 'Telangana', 'Karnataka']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Puducherry, Telangana, UP – 2024', width=550, height=320)
     return chart


</code>
"
726,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Madhya Pradesh stations in 2020, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Madhya Pradesh') & (data['Timestamp'].dt.year == 2020)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Madhya Pradesh Stations 2020', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Madhya Pradesh') & (data['Timestamp'].dt.year == 2020)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Madhya Pradesh Stations 2020', width=450, height=350)


</code>
"
727,spatial_aggregation,Show a bar chart of the top 6 cities by median PM2.5 in 2023.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2023]
    df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
    df = df.nlargest(6, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
        y=alt.Y('city:N', sort='-x', title='City'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
        tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 6 Cities by Median PM2.5 in 2023', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2023]
     df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
     df = df.nlargest(6, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
         y=alt.Y('city:N', sort='-x', title='City'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
         tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 6 Cities by Median PM2.5 in 2023', width=500, height=300)
     return chart


</code>
"
728,spatial_aggregation,Plot the top 6 states by average PM2.5 in 2024 as a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2024]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nlargest(6, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 6 States by Average PM2.5 in 2024', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2024]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nlargest(6, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 6 States by Average PM2.5 in 2024', width=500, height=300)
     return chart


</code>
"
729,specific_pattern,Show PM2.5 exceedances above the Indian standard (60 µg/m³) per year as a bar chart for Punjab.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Punjab'].dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 60].copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby('Year')['Timestamp'].nunique().reset_index()
    df.columns = ['Year','Days Exceeded']
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('Days Exceeded:Q', title='Days PM2.5 > 60 µg/m³'),
        tooltip=['Year:N','Days Exceeded:Q']
    ).properties(title='Days Punjab Exceeded Indian PM2.5 Standard (60 µg/m³) per Year', width=450, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Punjab'].dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 60].copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby('Year')['Timestamp'].nunique().reset_index()
     df.columns = ['Year','Days Exceeded']
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('Days Exceeded:Q', title='Days PM2.5 > 60 µg/m³'),
         tooltip=['Year:N','Days Exceeded:Q']
     ).properties(title='Days Punjab Exceeded Indian PM2.5 Standard (60 µg/m³) per Year', width=450, height=300)
     return chart


</code>
"
730,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Jammu and Kashmir, Jammu and Kashmir, and Haryana across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Jammu and Kashmir', 'Jammu and Kashmir', 'Haryana']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Jammu and Kashmir', 'Jammu and Kashmir', 'Haryana']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
731,spatio_temporal_aggregation,Show the monthly average PM2.5 for Jammu and Kashmir across each year from 2017 to 2024 as small multiples (faceted by year).,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Jammu and Kashmir'].copy()
    df['Year'] = df['Timestamp'].dt.year
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='steelblue').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5'),
        tooltip=['Year:O','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).facet(
        facet=alt.Facet('Year:O', title='Year'),
        columns=4
    ).properties(title='Monthly PM2.5 in Jammu and Kashmir by Year (2017–2024)')
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Jammu and Kashmir'].copy()
     df['Year'] = df['Timestamp'].dt.year
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='steelblue').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5'),
         tooltip=['Year:O','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).facet(
         facet=alt.Facet('Year:O', title='Year'),
         columns=4
     ).properties(title='Monthly PM2.5 in Jammu and Kashmir by Year (2017–2024)')
     return chart


</code>
"
732,temporal_aggregation,Show the monthly average PM10 trend for Mandikhera from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Mandikhera'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Mandikhera (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Mandikhera'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Mandikhera (2019–2024)', width=600, height=300)
     return chart


</code>
"
733,specific_pattern,Plot the rolling 30-day average PM2.5 for Arunachal Pradesh in 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Arunachal Pradesh') & (data['Timestamp'].dt.year == 2024)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Arunachal Pradesh 2024', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Arunachal Pradesh') & (data['Timestamp'].dt.year == 2024)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Arunachal Pradesh 2024', width=600, height=300)


</code>
"
734,spatial_aggregation,Visualize the bottom 15 states with the lowest average PM2.5 in 2017 using a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2017]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nsmallest(15, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Bottom 15 States by Average PM2.5 in 2017', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2017]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nsmallest(15, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Bottom 15 States by Average PM2.5 in 2017', width=500, height=300)
     return chart


</code>
"
735,temporal_aggregation,Show a monthly bar chart of the number of days Madhya Pradesh exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2021.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Madhya Pradesh') & (data['Timestamp'].dt.year == 2021)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Madhya Pradesh Exceeded WHO PM2.5 Guideline per Month – 2021', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Madhya Pradesh') & (data['Timestamp'].dt.year == 2021)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Madhya Pradesh Exceeded WHO PM2.5 Guideline per Month – 2021', width=500, height=300)
     return chart


</code>
"
736,spatial_aggregation,Plot the top 8 states by average PM2.5 in 2021 as a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2021]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nlargest(8, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 8 States by Average PM2.5 in 2021', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2021]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nlargest(8, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 8 States by Average PM2.5 in 2021', width=500, height=300)
     return chart


</code>
"
737,specific_pattern,Show a cumulative area chart of PM2.5 readings for Kolar across 2018.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Kolar') & (data['Timestamp'].dt.year == 2018)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Kolar 2018', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Kolar') & (data['Timestamp'].dt.year == 2018)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Kolar 2018', width=600, height=300)
     return chart


</code>
"
738,temporal_aggregation,Show a monthly bar chart of the number of days Uttarakhand exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2024.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Uttarakhand') & (data['Timestamp'].dt.year == 2024)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Uttarakhand Exceeded WHO PM2.5 Guideline per Month – 2024', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Uttarakhand') & (data['Timestamp'].dt.year == 2024)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Uttarakhand Exceeded WHO PM2.5 Guideline per Month – 2024', width=500, height=300)
     return chart


</code>
"
739,spatio_temporal_aggregation,Show a heatmap of average PM2.5 for the top 6 most polluted states by month for 2019.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    top8 = data.groupby('state')['PM2.5'].mean().nlargest(6).index.tolist()
    df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_rect().encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('state:N', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
        tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Heatmap – Top 6 Polluted States by Month (2019)', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     top8 = data.groupby('state')['PM2.5'].mean().nlargest(6).index.tolist()
     df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_rect().encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('state:N', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
         tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Heatmap – Top 6 Polluted States by Month (2019)', width=500, height=300)
     return chart


</code>
"
740,spatio_temporal_aggregation,Show a heatmap of average PM2.5 for the top 15 most polluted states by month for 2023.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    top8 = data.groupby('state')['PM2.5'].mean().nlargest(15).index.tolist()
    df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_rect().encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('state:N', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
        tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Heatmap – Top 15 Polluted States by Month (2023)', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     top8 = data.groupby('state')['PM2.5'].mean().nlargest(15).index.tolist()
     df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_rect().encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('state:N', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
         tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Heatmap – Top 15 Polluted States by Month (2023)', width=500, height=300)
     return chart


</code>
"
741,temporal_aggregation,Show a monthly bar chart of the number of days Karnataka exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2017.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Karnataka') & (data['Timestamp'].dt.year == 2017)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Karnataka Exceeded WHO PM2.5 Guideline per Month – 2017', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Karnataka') & (data['Timestamp'].dt.year == 2017)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Karnataka Exceeded WHO PM2.5 Guideline per Month – 2017', width=500, height=300)
     return chart


</code>
"
742,specific_pattern,Show a cumulative area chart of PM2.5 readings for Imphal across 2024.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Imphal') & (data['Timestamp'].dt.year == 2024)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Imphal 2024', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Imphal') & (data['Timestamp'].dt.year == 2024)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Imphal 2024', width=600, height=300)
     return chart


</code>
"
743,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Punjab, Andhra Pradesh, and Bihar across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Punjab', 'Andhra Pradesh', 'Bihar']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Punjab', 'Andhra Pradesh', 'Bihar']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
744,temporal_aggregation,Show the monthly average PM10 trend for Thoothukudi from 2017 to 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Thoothukudi'].copy()
    df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Thoothukudi (2017–2022)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Thoothukudi'].copy()
     df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Thoothukudi (2017–2022)', width=600, height=300)
     return chart


</code>
"
745,specific_pattern,Plot the rolling 30-day average PM2.5 for Andhra Pradesh in 2020 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Andhra Pradesh') & (data['Timestamp'].dt.year == 2020)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Andhra Pradesh 2020', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Andhra Pradesh') & (data['Timestamp'].dt.year == 2020)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Andhra Pradesh 2020', width=600, height=300)


</code>
"
746,spatial_aggregation,Plot the top 10 states by average PM2.5 in 2023 as a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2023]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nlargest(10, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 10 States by Average PM2.5 in 2023', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2023]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nlargest(10, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 10 States by Average PM2.5 in 2023', width=500, height=300)
     return chart


</code>
"
747,temporal_aggregation,Plot the monthly average PM2.5 trend for Tripura from 2017 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Tripura'].copy()
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='steelblue').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly Average PM2.5 Trend – Tripura (2017–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Tripura'].copy()
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='steelblue').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly Average PM2.5 Trend – Tripura (2017–2024)', width=600, height=300)
     return chart


</code>
"
748,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Gujarat, Gujarat, and Assam across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Gujarat', 'Gujarat', 'Assam']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Gujarat', 'Gujarat', 'Assam']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
749,spatio_temporal_aggregation,Create a heatmap showing the average PM2.5 by month (x-axis) and year (y-axis) for Jharkhand.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Jharkhand'].copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_rect().encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Year:N', title='Year'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='Avg PM2.5'),
        tooltip=['Year:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Heatmap – Jharkhand (Month × Year)', width=500, height=280)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Jharkhand'].copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_rect().encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Year:N', title='Year'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='Avg PM2.5'),
         tooltip=['Year:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Heatmap – Jharkhand (Month × Year)', width=500, height=280)
     return chart


</code>
"
750,spatio_temporal_aggregation,"Create a faceted bar chart showing top 11 states by average PM2.5 per year for 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year.isin([2019,2020,2021,2022])].copy()
    df['Year'] = df['Timestamp'].dt.year
    agg = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    top = agg.groupby('Year').apply(lambda x: x.nlargest(11,'PM2.5')).reset_index(drop=True)
    chart = alt.Chart(top).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Avg PM2.5'),
        y=alt.Y('state:N', sort='-x'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).facet(facet='Year:O', columns=2).properties(title='Top 11 States by PM2.5 per Year')
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year.isin([2019,2020,2021,2022])].copy()
     df['Year'] = df['Timestamp'].dt.year
     agg = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     top = agg.groupby('Year').apply(lambda x: x.nlargest(11,'PM2.5')).reset_index(drop=True)
     chart = alt.Chart(top).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Avg PM2.5'),
         y=alt.Y('state:N', sort='-x'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).facet(facet='Year:O', columns=2).properties(title='Top 11 States by PM2.5 per Year')
     return chart


</code>
"
751,temporal_aggregation,Show the monthly average PM10 trend for Begusarai from 2017 to 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Begusarai'].copy()
    df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Begusarai (2017–2022)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Begusarai'].copy()
     df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Begusarai (2017–2022)', width=600, height=300)
     return chart


</code>
"
752,spatial_aggregation,Plot the top 15 states by average PM2.5 in 2022 as a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2022]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nlargest(15, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 15 States by Average PM2.5 in 2022', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2022]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nlargest(15, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 15 States by Average PM2.5 in 2022', width=500, height=300)
     return chart


</code>
"
753,temporal_aggregation,Plot the weekly average PM2.5 for Damoh in 2023 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Damoh') & (data['Timestamp'].dt.year == 2023)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Damoh 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Damoh') & (data['Timestamp'].dt.year == 2023)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Damoh 2023', width=600, height=300)
     return chart


</code>
"
754,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Puducherry stations in 2021, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Puducherry') & (data['Timestamp'].dt.year == 2021)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Puducherry Stations 2021', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Puducherry') & (data['Timestamp'].dt.year == 2021)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Puducherry Stations 2021', width=450, height=350)


</code>
"
755,temporal_aggregation,Show the monthly average PM10 trend for Nandesari from 2017 to 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Nandesari'].copy()
    df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Nandesari (2017–2022)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Nandesari'].copy()
     df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Nandesari (2017–2022)', width=600, height=300)
     return chart


</code>
"
756,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Tamil Nadu, Gujarat, and Chandigarh across 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Tamil Nadu', 'Gujarat', 'Chandigarh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Tamil Nadu', 'Gujarat', 'Chandigarh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
     return chart


</code>
"
757,temporal_aggregation,Plot the weekly average PM2.5 for Talcher in 2021 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Talcher') & (data['Timestamp'].dt.year == 2021)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Talcher 2021', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Talcher') & (data['Timestamp'].dt.year == 2021)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Talcher 2021', width=600, height=300)
     return chart


</code>
"
758,temporal_aggregation,Show the monthly average PM2.5 for Nayagarh in 2023 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Nayagarh') & (data['Timestamp'].dt.year == 2023)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Nayagarh 2023', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Nayagarh') & (data['Timestamp'].dt.year == 2023)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Nayagarh 2023', width=450, height=280)


</code>
"
759,spatio_temporal_aggregation,Show the monthly average PM2.5 for Odisha across each year from 2017 to 2024 as small multiples (faceted by year).,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Odisha'].copy()
    df['Year'] = df['Timestamp'].dt.year
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='steelblue').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5'),
        tooltip=['Year:O','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).facet(
        facet=alt.Facet('Year:O', title='Year'),
        columns=4
    ).properties(title='Monthly PM2.5 in Odisha by Year (2017–2024)')
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Odisha'].copy()
     df['Year'] = df['Timestamp'].dt.year
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='steelblue').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5'),
         tooltip=['Year:O','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).facet(
         facet=alt.Facet('Year:O', title='Year'),
         columns=4
     ).properties(title='Monthly PM2.5 in Odisha by Year (2017–2024)')
     return chart


</code>
"
760,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Bihar, Karnataka, and Gujarat in 2021 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Bihar', 'Karnataka', 'Gujarat']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Bihar, Karnataka, UP – 2021', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Bihar', 'Karnataka', 'Gujarat']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Bihar, Karnataka, UP – 2021', width=550, height=320)
     return chart


</code>
"
761,spatial_aggregation,"Show the top 6 states by average PM10 in 2022 as a bar chart, sorted in descending order.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2022]
    df = df.groupby('state')['PM10'].mean().reset_index().dropna()
    df = df.nlargest(6, 'PM10')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Top 6 States by Average PM10 in 2022', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2022]
     df = df.groupby('state')['PM10'].mean().reset_index().dropna()
     df = df.nlargest(6, 'PM10')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Top 6 States by Average PM10 in 2022', width=500, height=300)
     return chart


</code>
"
762,spatial_aggregation,Plot the top 8 states by average PM2.5 in 2019 as a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2019]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nlargest(8, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 8 States by Average PM2.5 in 2019', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2019]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nlargest(8, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 8 States by Average PM2.5 in 2019', width=500, height=300)
     return chart


</code>
"
763,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Punjab stations in 2020, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Punjab') & (data['Timestamp'].dt.year == 2020)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Punjab Stations 2020', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Punjab') & (data['Timestamp'].dt.year == 2020)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Punjab Stations 2020', width=450, height=350)


</code>
"
764,spatial_aggregation,Plot the distribution of PM2.5 values in Meghalaya across all years using a histogram.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Meghalaya'].dropna(subset=['PM2.5'])
    df = df[['PM2.5']]
    chart = alt.Chart(df).mark_bar(color='steelblue', opacity=0.75).encode(
        x=alt.X('PM2\.5:Q', bin=alt.Bin(maxbins=40), title='PM2.5 (µg/m³)'),
        y=alt.Y('count()', title='Number of Observations'),
        tooltip=[alt.Tooltip('PM2\.5:Q', bin=True), 'count()']
    ).properties(title='PM2.5 Distribution – Meghalaya (All Years)', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Meghalaya'].dropna(subset=['PM2.5'])
     df = df[['PM2.5']]
     chart = alt.Chart(df).mark_bar(color='steelblue', opacity=0.75).encode(
         x=alt.X('PM2\.5:Q', bin=alt.Bin(maxbins=40), title='PM2.5 (µg/m³)'),
         y=alt.Y('count()', title='Number of Observations'),
         tooltip=[alt.Tooltip('PM2\.5:Q', bin=True), 'count()']
     ).properties(title='PM2.5 Distribution – Meghalaya (All Years)', width=500, height=300)
     return chart


</code>
"
765,spatio_temporal_aggregation,"Create a faceted bar chart showing top 13 states by average PM2.5 per year for 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year.isin([2017,2018,2019,2020])].copy()
    df['Year'] = df['Timestamp'].dt.year
    agg = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    top = agg.groupby('Year').apply(lambda x: x.nlargest(13,'PM2.5')).reset_index(drop=True)
    chart = alt.Chart(top).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Avg PM2.5'),
        y=alt.Y('state:N', sort='-x'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).facet(facet='Year:O', columns=2).properties(title='Top 13 States by PM2.5 per Year')
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year.isin([2017,2018,2019,2020])].copy()
     df['Year'] = df['Timestamp'].dt.year
     agg = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     top = agg.groupby('Year').apply(lambda x: x.nlargest(13,'PM2.5')).reset_index(drop=True)
     chart = alt.Chart(top).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Avg PM2.5'),
         y=alt.Y('state:N', sort='-x'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).facet(facet='Year:O', columns=2).properties(title='Top 13 States by PM2.5 per Year')
     return chart


</code>
"
766,temporal_aggregation,Plot the weekly average PM2.5 for Dausa in 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Dausa') & (data['Timestamp'].dt.year == 2024)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Dausa 2024', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Dausa') & (data['Timestamp'].dt.year == 2024)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Dausa 2024', width=600, height=300)
     return chart


</code>
"
767,specific_pattern,Plot the rolling 30-day average PM2.5 for Assam in 2023 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Assam') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Assam 2023', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Assam') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Assam 2023', width=600, height=300)


</code>
"
768,specific_pattern,Show PM2.5 exceedances above the Indian standard (60 µg/m³) per year as a bar chart for Rajasthan.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Rajasthan'].dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 60].copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby('Year')['Timestamp'].nunique().reset_index()
    df.columns = ['Year','Days Exceeded']
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('Days Exceeded:Q', title='Days PM2.5 > 60 µg/m³'),
        tooltip=['Year:N','Days Exceeded:Q']
    ).properties(title='Days Rajasthan Exceeded Indian PM2.5 Standard (60 µg/m³) per Year', width=450, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Rajasthan'].dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 60].copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby('Year')['Timestamp'].nunique().reset_index()
     df.columns = ['Year','Days Exceeded']
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('Days Exceeded:Q', title='Days PM2.5 > 60 µg/m³'),
         tooltip=['Year:N','Days Exceeded:Q']
     ).properties(title='Days Rajasthan Exceeded Indian PM2.5 Standard (60 µg/m³) per Year', width=450, height=300)
     return chart


</code>
"
769,temporal_aggregation,Show the monthly average PM10 trend for Dewas from 2017 to 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Dewas'].copy()
    df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Dewas (2017–2022)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Dewas'].copy()
     df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Dewas (2017–2022)', width=600, height=300)
     return chart


</code>
"
770,spatial_aggregation,"Show the top 11 states by average PM10 in 2020 as a bar chart, sorted in descending order.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2020]
    df = df.groupby('state')['PM10'].mean().reset_index().dropna()
    df = df.nlargest(11, 'PM10')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Top 11 States by Average PM10 in 2020', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2020]
     df = df.groupby('state')['PM10'].mean().reset_index().dropna()
     df = df.nlargest(11, 'PM10')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Top 11 States by Average PM10 in 2020', width=500, height=300)
     return chart


</code>
"
771,spatial_aggregation,Show a bar chart of the top 14 cities by median PM2.5 in 2019.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2019]
    df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
    df = df.nlargest(14, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
        y=alt.Y('city:N', sort='-x', title='City'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
        tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 14 Cities by Median PM2.5 in 2019', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2019]
     df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
     df = df.nlargest(14, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
         y=alt.Y('city:N', sort='-x', title='City'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
         tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 14 Cities by Median PM2.5 in 2019', width=500, height=300)
     return chart


</code>
"
772,spatial_aggregation,Plot the distribution of PM2.5 values in Assam across all years using a histogram.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Assam'].dropna(subset=['PM2.5'])
    df = df[['PM2.5']]
    chart = alt.Chart(df).mark_bar(color='steelblue', opacity=0.75).encode(
        x=alt.X('PM2\.5:Q', bin=alt.Bin(maxbins=40), title='PM2.5 (µg/m³)'),
        y=alt.Y('count()', title='Number of Observations'),
        tooltip=[alt.Tooltip('PM2\.5:Q', bin=True), 'count()']
    ).properties(title='PM2.5 Distribution – Assam (All Years)', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Assam'].dropna(subset=['PM2.5'])
     df = df[['PM2.5']]
     chart = alt.Chart(df).mark_bar(color='steelblue', opacity=0.75).encode(
         x=alt.X('PM2\.5:Q', bin=alt.Bin(maxbins=40), title='PM2.5 (µg/m³)'),
         y=alt.Y('count()', title='Number of Observations'),
         tooltip=[alt.Tooltip('PM2\.5:Q', bin=True), 'count()']
     ).properties(title='PM2.5 Distribution – Assam (All Years)', width=500, height=300)
     return chart


</code>
"
773,specific_pattern,Plot the rolling 30-day average PM2.5 for Bihar in 2023 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Bihar') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Bihar 2023', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Bihar') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Bihar 2023', width=600, height=300)


</code>
"
774,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Rajasthan, Andhra Pradesh, and Manipur from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Rajasthan', 'Andhra Pradesh', 'Manipur']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Rajasthan vs Andhra Pradesh vs Manipur', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Rajasthan', 'Andhra Pradesh', 'Manipur']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Rajasthan vs Andhra Pradesh vs Manipur', width=550, height=320)
     return chart


</code>
"
775,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Jharkhand, Maharashtra, and Karnataka in 2017 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Jharkhand', 'Maharashtra', 'Karnataka']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2017)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Jharkhand, Maharashtra, UP – 2017', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Jharkhand', 'Maharashtra', 'Karnataka']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2017)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Jharkhand, Maharashtra, UP – 2017', width=550, height=320)
     return chart


</code>
"
776,spatial_aggregation,Visualize the bottom 15 states with the lowest average PM2.5 in 2022 using a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2022]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nsmallest(15, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Bottom 15 States by Average PM2.5 in 2022', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2022]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nsmallest(15, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Bottom 15 States by Average PM2.5 in 2022', width=500, height=300)
     return chart


</code>
"
777,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Jammu and Kashmir stations in 2018, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Jammu and Kashmir') & (data['Timestamp'].dt.year == 2018)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Jammu and Kashmir Stations 2018', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Jammu and Kashmir') & (data['Timestamp'].dt.year == 2018)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Jammu and Kashmir Stations 2018', width=450, height=350)


</code>
"
778,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Rajasthan, Uttarakhand, and Jammu and Kashmir in 2021 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Rajasthan', 'Uttarakhand', 'Jammu and Kashmir']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Rajasthan, Uttarakhand, UP – 2021', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Rajasthan', 'Uttarakhand', 'Jammu and Kashmir']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Rajasthan, Uttarakhand, UP – 2021', width=550, height=320)
     return chart


</code>
"
779,temporal_aggregation,Plot the monthly average PM2.5 trend for Manipur from 2017 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Manipur'].copy()
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='steelblue').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly Average PM2.5 Trend – Manipur (2017–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Manipur'].copy()
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='steelblue').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly Average PM2.5 Trend – Manipur (2017–2024)', width=600, height=300)
     return chart


</code>
"
780,specific_pattern,Plot the rolling 30-day average PM2.5 for Chhattisgarh in 2017 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Chhattisgarh') & (data['Timestamp'].dt.year == 2017)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Chhattisgarh 2017', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Chhattisgarh') & (data['Timestamp'].dt.year == 2017)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Chhattisgarh 2017', width=600, height=300)


</code>
"
781,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Tamil Nadu, Arunachal Pradesh, and Manipur from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Tamil Nadu', 'Arunachal Pradesh', 'Manipur']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Tamil Nadu vs Arunachal Pradesh vs Manipur', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Tamil Nadu', 'Arunachal Pradesh', 'Manipur']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Tamil Nadu vs Arunachal Pradesh vs Manipur', width=550, height=320)
     return chart


</code>
"
782,spatio_temporal_aggregation,"Create a faceted bar chart showing top 12 states by average PM2.5 per year for 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year.isin([2019,2020,2021,2022])].copy()
    df['Year'] = df['Timestamp'].dt.year
    agg = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    top = agg.groupby('Year').apply(lambda x: x.nlargest(12,'PM2.5')).reset_index(drop=True)
    chart = alt.Chart(top).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Avg PM2.5'),
        y=alt.Y('state:N', sort='-x'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).facet(facet='Year:O', columns=2).properties(title='Top 12 States by PM2.5 per Year')
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year.isin([2019,2020,2021,2022])].copy()
     df['Year'] = df['Timestamp'].dt.year
     agg = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     top = agg.groupby('Year').apply(lambda x: x.nlargest(12,'PM2.5')).reset_index(drop=True)
     chart = alt.Chart(top).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Avg PM2.5'),
         y=alt.Y('state:N', sort='-x'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).facet(facet='Year:O', columns=2).properties(title='Top 12 States by PM2.5 per Year')
     return chart


</code>
"
783,spatio_temporal_aggregation,Show a heatmap of average PM2.5 for the top 12 most polluted states by month for 2022.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    top8 = data.groupby('state')['PM2.5'].mean().nlargest(12).index.tolist()
    df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_rect().encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('state:N', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
        tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Heatmap – Top 12 Polluted States by Month (2022)', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     top8 = data.groupby('state')['PM2.5'].mean().nlargest(12).index.tolist()
     df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_rect().encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('state:N', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
         tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Heatmap – Top 12 Polluted States by Month (2022)', width=500, height=300)
     return chart


</code>
"
784,temporal_aggregation,Show the monthly average PM2.5 for Varanasi in 2023 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Varanasi') & (data['Timestamp'].dt.year == 2023)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Varanasi 2023', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Varanasi') & (data['Timestamp'].dt.year == 2023)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Varanasi 2023', width=450, height=280)


</code>
"
785,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Uttarakhand, Andhra Pradesh, and Kerala in 2018 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Uttarakhand', 'Andhra Pradesh', 'Kerala']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2018)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Uttarakhand, Andhra Pradesh, UP – 2018', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Uttarakhand', 'Andhra Pradesh', 'Kerala']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2018)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Uttarakhand, Andhra Pradesh, UP – 2018', width=550, height=320)
     return chart


</code>
"
786,spatial_aggregation,"Show the top 13 states by average PM10 in 2018 as a bar chart, sorted in descending order.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2018]
    df = df.groupby('state')['PM10'].mean().reset_index().dropna()
    df = df.nlargest(13, 'PM10')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Top 13 States by Average PM10 in 2018', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2018]
     df = df.groupby('state')['PM10'].mean().reset_index().dropna()
     df = df.nlargest(13, 'PM10')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Top 13 States by Average PM10 in 2018', width=500, height=300)
     return chart


</code>
"
787,spatial_aggregation,Plot the top 15 states by average PM2.5 in 2024 as a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2024]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nlargest(15, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 15 States by Average PM2.5 in 2024', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2024]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nlargest(15, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 15 States by Average PM2.5 in 2024', width=500, height=300)
     return chart


</code>
"
788,spatio_temporal_aggregation,"Create a faceted bar chart showing top 8 states by average PM2.5 per year for 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year.isin([2017,2018,2019,2020])].copy()
    df['Year'] = df['Timestamp'].dt.year
    agg = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    top = agg.groupby('Year').apply(lambda x: x.nlargest(8,'PM2.5')).reset_index(drop=True)
    chart = alt.Chart(top).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Avg PM2.5'),
        y=alt.Y('state:N', sort='-x'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).facet(facet='Year:O', columns=2).properties(title='Top 8 States by PM2.5 per Year')
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year.isin([2017,2018,2019,2020])].copy()
     df['Year'] = df['Timestamp'].dt.year
     agg = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     top = agg.groupby('Year').apply(lambda x: x.nlargest(8,'PM2.5')).reset_index(drop=True)
     chart = alt.Chart(top).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Avg PM2.5'),
         y=alt.Y('state:N', sort='-x'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).facet(facet='Year:O', columns=2).properties(title='Top 8 States by PM2.5 per Year')
     return chart


</code>
"
789,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for West Bengal stations in 2020, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'West Bengal') & (data['Timestamp'].dt.year == 2020)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – West Bengal Stations 2020', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'West Bengal') & (data['Timestamp'].dt.year == 2020)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – West Bengal Stations 2020', width=450, height=350)


</code>
"
790,spatial_aggregation,Visualize the bottom 13 states with the lowest average PM2.5 in 2018 using a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2018]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nsmallest(13, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Bottom 13 States by Average PM2.5 in 2018', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2018]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nsmallest(13, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Bottom 13 States by Average PM2.5 in 2018', width=500, height=300)
     return chart


</code>
"
791,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Uttar Pradesh stations in 2023, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Uttar Pradesh') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Uttar Pradesh Stations 2023', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Uttar Pradesh') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Uttar Pradesh Stations 2023', width=450, height=350)


</code>
"
792,spatio_temporal_aggregation,Show a heatmap of average PM2.5 for the top 6 most polluted states by month for 2021.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    top8 = data.groupby('state')['PM2.5'].mean().nlargest(6).index.tolist()
    df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2021)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_rect().encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('state:N', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
        tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Heatmap – Top 6 Polluted States by Month (2021)', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     top8 = data.groupby('state')['PM2.5'].mean().nlargest(6).index.tolist()
     df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2021)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_rect().encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('state:N', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
         tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Heatmap – Top 6 Polluted States by Month (2021)', width=500, height=300)
     return chart


</code>
"
793,temporal_aggregation,Show the monthly average PM10 trend for Khanna from 2017 to 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Khanna'].copy()
    df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Khanna (2017–2022)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Khanna'].copy()
     df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Khanna (2017–2022)', width=600, height=300)
     return chart


</code>
"
794,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Ludhiana, Noida, and Kozhikode in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Ludhiana', 'Noida', 'Kozhikode']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Ludhiana vs Noida vs Kozhikode – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Ludhiana', 'Noida', 'Kozhikode']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Ludhiana vs Noida vs Kozhikode – 2024', width=550, height=320)
     return chart


</code>
"
795,spatio_temporal_aggregation,Create a heatmap showing the average PM2.5 by month (x-axis) and year (y-axis) for Madhya Pradesh.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Madhya Pradesh'].copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_rect().encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Year:N', title='Year'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='Avg PM2.5'),
        tooltip=['Year:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Heatmap – Madhya Pradesh (Month × Year)', width=500, height=280)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Madhya Pradesh'].copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_rect().encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Year:N', title='Year'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='Avg PM2.5'),
         tooltip=['Year:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Heatmap – Madhya Pradesh (Month × Year)', width=500, height=280)
     return chart


</code>
"
796,spatial_aggregation,Show a bar chart of the top 7 cities by median PM2.5 in 2018.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2018]
    df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
    df = df.nlargest(7, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
        y=alt.Y('city:N', sort='-x', title='City'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
        tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 7 Cities by Median PM2.5 in 2018', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2018]
     df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
     df = df.nlargest(7, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
         y=alt.Y('city:N', sort='-x', title='City'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
         tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 7 Cities by Median PM2.5 in 2018', width=500, height=300)
     return chart


</code>
"
797,temporal_aggregation,Plot the weekly average PM2.5 for Kollam in 2020 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Kollam') & (data['Timestamp'].dt.year == 2020)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Kollam 2020', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Kollam') & (data['Timestamp'].dt.year == 2020)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Kollam 2020', width=600, height=300)
     return chart


</code>
"
798,temporal_aggregation,Show a monthly bar chart of the number of days Puducherry exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2024.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Puducherry') & (data['Timestamp'].dt.year == 2024)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Puducherry Exceeded WHO PM2.5 Guideline per Month – 2024', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Puducherry') & (data['Timestamp'].dt.year == 2024)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Puducherry Exceeded WHO PM2.5 Guideline per Month – 2024', width=500, height=300)
     return chart


</code>
"
799,spatio_temporal_aggregation,Create a heatmap showing the average PM2.5 by month (x-axis) and year (y-axis) for Chandigarh.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Chandigarh'].copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_rect().encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Year:N', title='Year'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='Avg PM2.5'),
        tooltip=['Year:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Heatmap – Chandigarh (Month × Year)', width=500, height=280)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Chandigarh'].copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_rect().encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Year:N', title='Year'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='Avg PM2.5'),
         tooltip=['Year:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Heatmap – Chandigarh (Month × Year)', width=500, height=280)
     return chart


</code>
"
800,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Manipur, Karnataka, and Arunachal Pradesh in 2019 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Manipur', 'Karnataka', 'Arunachal Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Manipur, Karnataka, UP – 2019', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Manipur', 'Karnataka', 'Arunachal Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Manipur, Karnataka, UP – 2019', width=550, height=320)
     return chart


</code>
"
801,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Chandigarh, Telangana, and Delhi from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Chandigarh', 'Telangana', 'Delhi']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Chandigarh vs Telangana vs Delhi', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Chandigarh', 'Telangana', 'Delhi']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Chandigarh vs Telangana vs Delhi', width=550, height=320)
     return chart


</code>
"
802,specific_pattern,Plot the rolling 30-day average PM2.5 for West Bengal in 2023 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'West Bengal') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – West Bengal 2023', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'West Bengal') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – West Bengal 2023', width=600, height=300)


</code>
"
803,spatial_aggregation,Show a bar chart of the top 6 cities by median PM2.5 in 2017.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2017]
    df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
    df = df.nlargest(6, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
        y=alt.Y('city:N', sort='-x', title='City'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
        tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 6 Cities by Median PM2.5 in 2017', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2017]
     df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
     df = df.nlargest(6, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
         y=alt.Y('city:N', sort='-x', title='City'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
         tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 6 Cities by Median PM2.5 in 2017', width=500, height=300)
     return chart


</code>
"
804,temporal_aggregation,Show the monthly average PM2.5 for Rajsamand in 2023 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Rajsamand') & (data['Timestamp'].dt.year == 2023)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Rajsamand 2023', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Rajsamand') & (data['Timestamp'].dt.year == 2023)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Rajsamand 2023', width=450, height=280)


</code>
"
805,temporal_aggregation,Show a monthly bar chart of the number of days Kerala exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2022.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Kerala') & (data['Timestamp'].dt.year == 2022)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Kerala Exceeded WHO PM2.5 Guideline per Month – 2022', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Kerala') & (data['Timestamp'].dt.year == 2022)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Kerala Exceeded WHO PM2.5 Guideline per Month – 2022', width=500, height=300)
     return chart


</code>
"
806,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Jharkhand, Uttarakhand, and Delhi across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Jharkhand', 'Uttarakhand', 'Delhi']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Jharkhand', 'Uttarakhand', 'Delhi']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
807,spatial_aggregation,Visualize the bottom 14 states with the lowest average PM2.5 in 2022 using a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2022]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nsmallest(14, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Bottom 14 States by Average PM2.5 in 2022', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2022]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nsmallest(14, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Bottom 14 States by Average PM2.5 in 2022', width=500, height=300)
     return chart


</code>
"
808,spatio_temporal_aggregation,Show the monthly average PM2.5 for Maharashtra across each year from 2017 to 2024 as small multiples (faceted by year).,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Maharashtra'].copy()
    df['Year'] = df['Timestamp'].dt.year
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='steelblue').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5'),
        tooltip=['Year:O','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).facet(
        facet=alt.Facet('Year:O', title='Year'),
        columns=4
    ).properties(title='Monthly PM2.5 in Maharashtra by Year (2017–2024)')
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Maharashtra'].copy()
     df['Year'] = df['Timestamp'].dt.year
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='steelblue').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5'),
         tooltip=['Year:O','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).facet(
         facet=alt.Facet('Year:O', title='Year'),
         columns=4
     ).properties(title='Monthly PM2.5 in Maharashtra by Year (2017–2024)')
     return chart


</code>
"
809,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Maharashtra stations in 2021, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Maharashtra') & (data['Timestamp'].dt.year == 2021)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Maharashtra Stations 2021', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Maharashtra') & (data['Timestamp'].dt.year == 2021)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Maharashtra Stations 2021', width=450, height=350)


</code>
"
810,temporal_aggregation,Show the monthly average PM10 trend for Gorakhpur from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Gorakhpur'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Gorakhpur (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Gorakhpur'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Gorakhpur (2019–2024)', width=600, height=300)
     return chart


</code>
"
811,spatial_aggregation,Plot the distribution of PM2.5 values in Tamil Nadu across all years using a histogram.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Tamil Nadu'].dropna(subset=['PM2.5'])
    df = df[['PM2.5']]
    chart = alt.Chart(df).mark_bar(color='steelblue', opacity=0.75).encode(
        x=alt.X('PM2\.5:Q', bin=alt.Bin(maxbins=40), title='PM2.5 (µg/m³)'),
        y=alt.Y('count()', title='Number of Observations'),
        tooltip=[alt.Tooltip('PM2\.5:Q', bin=True), 'count()']
    ).properties(title='PM2.5 Distribution – Tamil Nadu (All Years)', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Tamil Nadu'].dropna(subset=['PM2.5'])
     df = df[['PM2.5']]
     chart = alt.Chart(df).mark_bar(color='steelblue', opacity=0.75).encode(
         x=alt.X('PM2\.5:Q', bin=alt.Bin(maxbins=40), title='PM2.5 (µg/m³)'),
         y=alt.Y('count()', title='Number of Observations'),
         tooltip=[alt.Tooltip('PM2\.5:Q', bin=True), 'count()']
     ).properties(title='PM2.5 Distribution – Tamil Nadu (All Years)', width=500, height=300)
     return chart


</code>
"
812,spatial_aggregation,"Show the top 14 states by average PM10 in 2020 as a bar chart, sorted in descending order.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2020]
    df = df.groupby('state')['PM10'].mean().reset_index().dropna()
    df = df.nlargest(14, 'PM10')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Top 14 States by Average PM10 in 2020', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2020]
     df = df.groupby('state')['PM10'].mean().reset_index().dropna()
     df = df.nlargest(14, 'PM10')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Top 14 States by Average PM10 in 2020', width=500, height=300)
     return chart


</code>
"
813,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Andhra Pradesh, Tamil Nadu, and Uttar Pradesh from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Andhra Pradesh', 'Tamil Nadu', 'Uttar Pradesh']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Andhra Pradesh vs Tamil Nadu vs Uttar Pradesh', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Andhra Pradesh', 'Tamil Nadu', 'Uttar Pradesh']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Andhra Pradesh vs Tamil Nadu vs Uttar Pradesh', width=550, height=320)
     return chart


</code>
"
814,temporal_aggregation,Show a monthly bar chart of the number of days Gujarat exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2017.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Gujarat') & (data['Timestamp'].dt.year == 2017)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Gujarat Exceeded WHO PM2.5 Guideline per Month – 2017', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Gujarat') & (data['Timestamp'].dt.year == 2017)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Gujarat Exceeded WHO PM2.5 Guideline per Month – 2017', width=500, height=300)
     return chart


</code>
"
815,specific_pattern,Show PM2.5 exceedances above the Indian standard (60 µg/m³) per year as a bar chart for Gujarat.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Gujarat'].dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 60].copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby('Year')['Timestamp'].nunique().reset_index()
    df.columns = ['Year','Days Exceeded']
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('Days Exceeded:Q', title='Days PM2.5 > 60 µg/m³'),
        tooltip=['Year:N','Days Exceeded:Q']
    ).properties(title='Days Gujarat Exceeded Indian PM2.5 Standard (60 µg/m³) per Year', width=450, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Gujarat'].dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 60].copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby('Year')['Timestamp'].nunique().reset_index()
     df.columns = ['Year','Days Exceeded']
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('Days Exceeded:Q', title='Days PM2.5 > 60 µg/m³'),
         tooltip=['Year:N','Days Exceeded:Q']
     ).properties(title='Days Gujarat Exceeded Indian PM2.5 Standard (60 µg/m³) per Year', width=450, height=300)
     return chart


</code>
"
816,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Mizoram, Tamil Nadu, and Tripura from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Mizoram', 'Tamil Nadu', 'Tripura']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Mizoram vs Tamil Nadu vs Tripura', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Mizoram', 'Tamil Nadu', 'Tripura']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Mizoram vs Tamil Nadu vs Tripura', width=550, height=320)
     return chart


</code>
"
817,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Tamil Nadu, Jammu and Kashmir, and Rajasthan in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Tamil Nadu', 'Jammu and Kashmir', 'Rajasthan']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Tamil Nadu, Jammu and Kashmir, UP – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Tamil Nadu', 'Jammu and Kashmir', 'Rajasthan']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Tamil Nadu, Jammu and Kashmir, UP – 2022', width=550, height=320)
     return chart


</code>
"
818,specific_pattern,Show a cumulative area chart of PM2.5 readings for Yadgir across 2019.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Yadgir') & (data['Timestamp'].dt.year == 2019)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Yadgir 2019', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Yadgir') & (data['Timestamp'].dt.year == 2019)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Yadgir 2019', width=600, height=300)
     return chart


</code>
"
819,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Nagaland, Andhra Pradesh, and Gujarat from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Nagaland', 'Andhra Pradesh', 'Gujarat']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Nagaland vs Andhra Pradesh vs Gujarat', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Nagaland', 'Andhra Pradesh', 'Gujarat']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Nagaland vs Andhra Pradesh vs Gujarat', width=550, height=320)
     return chart


</code>
"
820,spatial_aggregation,Plot the top 14 states by average PM2.5 in 2019 as a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2019]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nlargest(14, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 14 States by Average PM2.5 in 2019', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2019]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nlargest(14, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 14 States by Average PM2.5 in 2019', width=500, height=300)
     return chart


</code>
"
821,temporal_aggregation,Show the monthly average PM2.5 for Chennai in 2024 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Chennai') & (data['Timestamp'].dt.year == 2024)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Chennai 2024', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Chennai') & (data['Timestamp'].dt.year == 2024)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Chennai 2024', width=450, height=280)


</code>
"
822,spatio_temporal_aggregation,Show a heatmap of average PM2.5 for the top 10 most polluted states by month for 2017.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    top8 = data.groupby('state')['PM2.5'].mean().nlargest(10).index.tolist()
    df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2017)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_rect().encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('state:N', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
        tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Heatmap – Top 10 Polluted States by Month (2017)', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     top8 = data.groupby('state')['PM2.5'].mean().nlargest(10).index.tolist()
     df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2017)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_rect().encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('state:N', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
         tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Heatmap – Top 10 Polluted States by Month (2017)', width=500, height=300)
     return chart


</code>
"
823,temporal_aggregation,Show a monthly bar chart of the number of days West Bengal exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2023.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'West Bengal') & (data['Timestamp'].dt.year == 2023)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days West Bengal Exceeded WHO PM2.5 Guideline per Month – 2023', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'West Bengal') & (data['Timestamp'].dt.year == 2023)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days West Bengal Exceeded WHO PM2.5 Guideline per Month – 2023', width=500, height=300)
     return chart


</code>
"
824,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Odisha, Chhattisgarh, and Jammu and Kashmir across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Odisha', 'Chhattisgarh', 'Jammu and Kashmir']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Odisha', 'Chhattisgarh', 'Jammu and Kashmir']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
825,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Puducherry, Telangana, and Mizoram from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Puducherry', 'Telangana', 'Mizoram']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Puducherry vs Telangana vs Mizoram', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Puducherry', 'Telangana', 'Mizoram']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Puducherry vs Telangana vs Mizoram', width=550, height=320)
     return chart


</code>
"
826,spatial_aggregation,Plot the top 14 states by average PM2.5 in 2023 as a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2023]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nlargest(14, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 14 States by Average PM2.5 in 2023', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2023]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nlargest(14, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 14 States by Average PM2.5 in 2023', width=500, height=300)
     return chart


</code>
"
827,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Jharkhand, Assam, and Haryana in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Jharkhand', 'Assam', 'Haryana']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Jharkhand, Assam, UP – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Jharkhand', 'Assam', 'Haryana']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Jharkhand, Assam, UP – 2023', width=550, height=320)
     return chart


</code>
"
828,spatial_aggregation,Plot the top 11 states by average PM2.5 in 2017 as a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2017]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nlargest(11, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 11 States by Average PM2.5 in 2017', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2017]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nlargest(11, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 11 States by Average PM2.5 in 2017', width=500, height=300)
     return chart


</code>
"
829,temporal_aggregation,Show a monthly bar chart of the number of days Maharashtra exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2021.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Maharashtra') & (data['Timestamp'].dt.year == 2021)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Maharashtra Exceeded WHO PM2.5 Guideline per Month – 2021', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Maharashtra') & (data['Timestamp'].dt.year == 2021)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Maharashtra Exceeded WHO PM2.5 Guideline per Month – 2021', width=500, height=300)
     return chart


</code>
"
830,specific_pattern,Plot the rolling 30-day average PM2.5 for Chhattisgarh in 2018 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Chhattisgarh') & (data['Timestamp'].dt.year == 2018)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Chhattisgarh 2018', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Chhattisgarh') & (data['Timestamp'].dt.year == 2018)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Chhattisgarh 2018', width=600, height=300)


</code>
"
831,spatial_aggregation,Visualize the bottom 5 states with the lowest average PM2.5 in 2019 using a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2019]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nsmallest(5, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Bottom 5 States by Average PM2.5 in 2019', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2019]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nsmallest(5, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Bottom 5 States by Average PM2.5 in 2019', width=500, height=300)
     return chart


</code>
"
832,temporal_aggregation,Show a monthly bar chart of the number of days Uttar Pradesh exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2022.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Uttar Pradesh') & (data['Timestamp'].dt.year == 2022)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Uttar Pradesh Exceeded WHO PM2.5 Guideline per Month – 2022', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Uttar Pradesh') & (data['Timestamp'].dt.year == 2022)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Uttar Pradesh Exceeded WHO PM2.5 Guideline per Month – 2022', width=500, height=300)
     return chart


</code>
"
833,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Arunachal Pradesh, Jammu and Kashmir, and Bihar across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Arunachal Pradesh', 'Jammu and Kashmir', 'Bihar']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Arunachal Pradesh', 'Jammu and Kashmir', 'Bihar']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
834,specific_pattern,Show a cumulative area chart of PM2.5 readings for Ujjain across 2022.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Ujjain') & (data['Timestamp'].dt.year == 2022)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Ujjain 2022', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Ujjain') & (data['Timestamp'].dt.year == 2022)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Ujjain 2022', width=600, height=300)
     return chart


</code>
"
835,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Jabalpur, Kochi, and Karwar in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Jabalpur', 'Kochi', 'Karwar']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Jabalpur vs Kochi vs Karwar – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Jabalpur', 'Kochi', 'Karwar']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Jabalpur vs Kochi vs Karwar – 2023', width=550, height=320)
     return chart


</code>
"
836,spatial_aggregation,Plot the top 6 states by average PM2.5 in 2021 as a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2021]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nlargest(6, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 6 States by Average PM2.5 in 2021', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2021]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nlargest(6, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 6 States by Average PM2.5 in 2021', width=500, height=300)
     return chart


</code>
"
837,temporal_aggregation,Show the monthly average PM2.5 for Angul in 2017 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Angul') & (data['Timestamp'].dt.year == 2017)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Angul 2017', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Angul') & (data['Timestamp'].dt.year == 2017)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Angul 2017', width=450, height=280)


</code>
"
838,temporal_aggregation,Plot the weekly average PM2.5 for Khanna in 2018 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Khanna') & (data['Timestamp'].dt.year == 2018)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Khanna 2018', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Khanna') & (data['Timestamp'].dt.year == 2018)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Khanna 2018', width=600, height=300)
     return chart


</code>
"
839,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Faridabad, Nashik, and Vapi in 2017 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Faridabad', 'Nashik', 'Vapi']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2017)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Faridabad vs Nashik vs Vapi – 2017', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Faridabad', 'Nashik', 'Vapi']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2017)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Faridabad vs Nashik vs Vapi – 2017', width=550, height=320)
     return chart


</code>
"
840,spatio_temporal_aggregation,Show a heatmap of average PM2.5 for the top 10 most polluted states by month for 2021.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    top8 = data.groupby('state')['PM2.5'].mean().nlargest(10).index.tolist()
    df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2021)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_rect().encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('state:N', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
        tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Heatmap – Top 10 Polluted States by Month (2021)', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     top8 = data.groupby('state')['PM2.5'].mean().nlargest(10).index.tolist()
     df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2021)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_rect().encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('state:N', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
         tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Heatmap – Top 10 Polluted States by Month (2021)', width=500, height=300)
     return chart


</code>
"
841,specific_pattern,Show a cumulative area chart of PM2.5 readings for Bidar across 2023.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Bidar') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Bidar 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Bidar') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Bidar 2023', width=600, height=300)
     return chart


</code>
"
842,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Meghalaya, Telangana, and Arunachal Pradesh from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Meghalaya', 'Telangana', 'Arunachal Pradesh']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Meghalaya vs Telangana vs Arunachal Pradesh', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Meghalaya', 'Telangana', 'Arunachal Pradesh']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Meghalaya vs Telangana vs Arunachal Pradesh', width=550, height=320)
     return chart


</code>
"
843,temporal_aggregation,Show a monthly bar chart of the number of days Tamil Nadu exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2018.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Tamil Nadu') & (data['Timestamp'].dt.year == 2018)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Tamil Nadu Exceeded WHO PM2.5 Guideline per Month – 2018', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Tamil Nadu') & (data['Timestamp'].dt.year == 2018)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Tamil Nadu Exceeded WHO PM2.5 Guideline per Month – 2018', width=500, height=300)
     return chart


</code>
"
844,temporal_aggregation,Show the monthly average PM2.5 for Badlapur in 2018 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Badlapur') & (data['Timestamp'].dt.year == 2018)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Badlapur 2018', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Badlapur') & (data['Timestamp'].dt.year == 2018)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Badlapur 2018', width=450, height=280)


</code>
"
845,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Puducherry, Delhi, and Kerala across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Puducherry', 'Delhi', 'Kerala']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Puducherry', 'Delhi', 'Kerala']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
846,spatial_aggregation,Show a bar chart of the top 11 cities by median PM2.5 in 2017.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2017]
    df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
    df = df.nlargest(11, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
        y=alt.Y('city:N', sort='-x', title='City'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
        tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 11 Cities by Median PM2.5 in 2017', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2017]
     df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
     df = df.nlargest(11, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
         y=alt.Y('city:N', sort='-x', title='City'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
         tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 11 Cities by Median PM2.5 in 2017', width=500, height=300)
     return chart


</code>
"
847,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Kerala stations in 2017, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Kerala') & (data['Timestamp'].dt.year == 2017)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Kerala Stations 2017', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Kerala') & (data['Timestamp'].dt.year == 2017)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Kerala Stations 2017', width=450, height=350)


</code>
"
848,area_based,"Create a bubble chart of PM2.5 vs area for each state in 2024, sized by population.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    pm = data[data['Timestamp'].dt.year == 2024].groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = pm.merge(states_data, on='state')
    chart = alt.Chart(df).mark_point(filled=True, opacity=0.7).encode(
        x=alt.X('area (km2):Q', title='Area (km²)', axis=alt.Axis(format='~s')),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        size=alt.Size('population:Q', title='Population', scale=alt.Scale(range=[50,1500])),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f'),
                 alt.Tooltip('population:Q', format=','), alt.Tooltip('area (km2):Q', format=',')]
    ).properties(title='PM2.5 vs Area (size=Population) – 2024', width=500, height=400)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     pm = data[data['Timestamp'].dt.year == 2024].groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = pm.merge(states_data, on='state')
     chart = alt.Chart(df).mark_point(filled=True, opacity=0.7).encode(
         x=alt.X('area (km2):Q', title='Area (km²)', axis=alt.Axis(format='~s')),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         size=alt.Size('population:Q', title='Population', scale=alt.Scale(range=[50,1500])),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f'),
                  alt.Tooltip('population:Q', format=','), alt.Tooltip('area (km2):Q', format=',')]
     ).properties(title='PM2.5 vs Area (size=Population) – 2024', width=500, height=400)
     return chart


</code>
"
849,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Aurangabad, Satna, and Siliguri in 2020 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Aurangabad', 'Satna', 'Siliguri']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2020)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Aurangabad vs Satna vs Siliguri – 2020', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Aurangabad', 'Satna', 'Siliguri']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2020)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Aurangabad vs Satna vs Siliguri – 2020', width=550, height=320)
     return chart


</code>
"
850,spatio_temporal_aggregation,"Create a faceted bar chart showing top 9 states by average PM2.5 per year for 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year.isin([2020,2021,2022,2023])].copy()
    df['Year'] = df['Timestamp'].dt.year
    agg = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    top = agg.groupby('Year').apply(lambda x: x.nlargest(9,'PM2.5')).reset_index(drop=True)
    chart = alt.Chart(top).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Avg PM2.5'),
        y=alt.Y('state:N', sort='-x'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).facet(facet='Year:O', columns=2).properties(title='Top 9 States by PM2.5 per Year')
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year.isin([2020,2021,2022,2023])].copy()
     df['Year'] = df['Timestamp'].dt.year
     agg = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     top = agg.groupby('Year').apply(lambda x: x.nlargest(9,'PM2.5')).reset_index(drop=True)
     chart = alt.Chart(top).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Avg PM2.5'),
         y=alt.Y('state:N', sort='-x'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).facet(facet='Year:O', columns=2).properties(title='Top 9 States by PM2.5 per Year')
     return chart


</code>
"
851,specific_pattern,Show a cumulative area chart of PM2.5 readings for Bihar Sharif across 2023.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Bihar Sharif') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Bihar Sharif 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Bihar Sharif') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Bihar Sharif 2023', width=600, height=300)
     return chart


</code>
"
852,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Chandigarh, Rajasthan, and Himachal Pradesh from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Chandigarh', 'Rajasthan', 'Himachal Pradesh']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Chandigarh vs Rajasthan vs Himachal Pradesh', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Chandigarh', 'Rajasthan', 'Himachal Pradesh']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Chandigarh vs Rajasthan vs Himachal Pradesh', width=550, height=320)
     return chart


</code>
"
853,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Maharashtra stations in 2023, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Maharashtra') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Maharashtra Stations 2023', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Maharashtra') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Maharashtra Stations 2023', width=450, height=350)


</code>
"
854,spatio_temporal_aggregation,Show a heatmap of average PM2.5 for the top 5 most polluted states by month for 2024.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    top8 = data.groupby('state')['PM2.5'].mean().nlargest(5).index.tolist()
    df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_rect().encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('state:N', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
        tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Heatmap – Top 5 Polluted States by Month (2024)', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     top8 = data.groupby('state')['PM2.5'].mean().nlargest(5).index.tolist()
     df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_rect().encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('state:N', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
         tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Heatmap – Top 5 Polluted States by Month (2024)', width=500, height=300)
     return chart


</code>
"
855,temporal_aggregation,Show the monthly average PM2.5 for Gorakhpur in 2024 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Gorakhpur') & (data['Timestamp'].dt.year == 2024)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Gorakhpur 2024', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Gorakhpur') & (data['Timestamp'].dt.year == 2024)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Gorakhpur 2024', width=450, height=280)


</code>
"
856,spatial_aggregation,Visualize the bottom 5 states with the lowest average PM2.5 in 2020 using a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2020]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nsmallest(5, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Bottom 5 States by Average PM2.5 in 2020', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2020]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nsmallest(5, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Bottom 5 States by Average PM2.5 in 2020', width=500, height=300)
     return chart


</code>
"
857,spatio_temporal_aggregation,Show a heatmap of average PM2.5 for the top 8 most polluted states by month for 2023.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    top8 = data.groupby('state')['PM2.5'].mean().nlargest(8).index.tolist()
    df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_rect().encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('state:N', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
        tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Heatmap – Top 8 Polluted States by Month (2023)', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     top8 = data.groupby('state')['PM2.5'].mean().nlargest(8).index.tolist()
     df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_rect().encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('state:N', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
         tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Heatmap – Top 8 Polluted States by Month (2023)', width=500, height=300)
     return chart


</code>
"
858,spatial_aggregation,"Show the top 10 states by average PM10 in 2018 as a bar chart, sorted in descending order.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2018]
    df = df.groupby('state')['PM10'].mean().reset_index().dropna()
    df = df.nlargest(10, 'PM10')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Top 10 States by Average PM10 in 2018', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2018]
     df = df.groupby('state')['PM10'].mean().reset_index().dropna()
     df = df.nlargest(10, 'PM10')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Top 10 States by Average PM10 in 2018', width=500, height=300)
     return chart


</code>
"
859,temporal_aggregation,Show a monthly bar chart of the number of days Uttar Pradesh exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2024.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Uttar Pradesh') & (data['Timestamp'].dt.year == 2024)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Uttar Pradesh Exceeded WHO PM2.5 Guideline per Month – 2024', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Uttar Pradesh') & (data['Timestamp'].dt.year == 2024)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Uttar Pradesh Exceeded WHO PM2.5 Guideline per Month – 2024', width=500, height=300)
     return chart


</code>
"
860,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Ankleshwar, Kashipur, and Bhiwani in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Ankleshwar', 'Kashipur', 'Bhiwani']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Ankleshwar vs Kashipur vs Bhiwani – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Ankleshwar', 'Kashipur', 'Bhiwani']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Ankleshwar vs Kashipur vs Bhiwani – 2023', width=550, height=320)
     return chart


</code>
"
861,temporal_aggregation,Show a monthly bar chart of the number of days Mizoram exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2024.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Mizoram') & (data['Timestamp'].dt.year == 2024)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Mizoram Exceeded WHO PM2.5 Guideline per Month – 2024', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Mizoram') & (data['Timestamp'].dt.year == 2024)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Mizoram Exceeded WHO PM2.5 Guideline per Month – 2024', width=500, height=300)
     return chart


</code>
"
862,temporal_aggregation,Plot the weekly average PM2.5 for Haveri in 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Haveri') & (data['Timestamp'].dt.year == 2024)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Haveri 2024', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Haveri') & (data['Timestamp'].dt.year == 2024)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Haveri 2024', width=600, height=300)
     return chart


</code>
"
863,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Puducherry, Haryana, and Delhi from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Puducherry', 'Haryana', 'Delhi']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Puducherry vs Haryana vs Delhi', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Puducherry', 'Haryana', 'Delhi']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Puducherry vs Haryana vs Delhi', width=550, height=320)
     return chart


</code>
"
864,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Andhra Pradesh stations in 2021, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Andhra Pradesh') & (data['Timestamp'].dt.year == 2021)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Andhra Pradesh Stations 2021', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Andhra Pradesh') & (data['Timestamp'].dt.year == 2021)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Andhra Pradesh Stations 2021', width=450, height=350)


</code>
"
865,temporal_aggregation,Show the monthly average PM10 trend for Aizawl from 2017 to 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Aizawl'].copy()
    df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Aizawl (2017–2022)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Aizawl'].copy()
     df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Aizawl (2017–2022)', width=600, height=300)
     return chart


</code>
"
866,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Haryana, Tamil Nadu, and Nagaland from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Haryana', 'Tamil Nadu', 'Nagaland']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Haryana vs Tamil Nadu vs Nagaland', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Haryana', 'Tamil Nadu', 'Nagaland']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Haryana vs Tamil Nadu vs Nagaland', width=550, height=320)
     return chart


</code>
"
867,spatio_temporal_aggregation,"Create a faceted bar chart showing top 11 states by average PM2.5 per year for 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year.isin([2020,2021,2022,2023])].copy()
    df['Year'] = df['Timestamp'].dt.year
    agg = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    top = agg.groupby('Year').apply(lambda x: x.nlargest(11,'PM2.5')).reset_index(drop=True)
    chart = alt.Chart(top).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Avg PM2.5'),
        y=alt.Y('state:N', sort='-x'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).facet(facet='Year:O', columns=2).properties(title='Top 11 States by PM2.5 per Year')
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year.isin([2020,2021,2022,2023])].copy()
     df['Year'] = df['Timestamp'].dt.year
     agg = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     top = agg.groupby('Year').apply(lambda x: x.nlargest(11,'PM2.5')).reset_index(drop=True)
     chart = alt.Chart(top).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Avg PM2.5'),
         y=alt.Y('state:N', sort='-x'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).facet(facet='Year:O', columns=2).properties(title='Top 11 States by PM2.5 per Year')
     return chart


</code>
"
868,temporal_aggregation,Plot the weekly average PM2.5 for Kolar in 2020 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Kolar') & (data['Timestamp'].dt.year == 2020)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Kolar 2020', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Kolar') & (data['Timestamp'].dt.year == 2020)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Kolar 2020', width=600, height=300)
     return chart


</code>
"
869,specific_pattern,Plot the rolling 30-day average PM2.5 for Telangana in 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Telangana') & (data['Timestamp'].dt.year == 2022)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Telangana 2022', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Telangana') & (data['Timestamp'].dt.year == 2022)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Telangana 2022', width=600, height=300)


</code>
"
870,spatial_aggregation,Show a bar chart of the top 9 cities by median PM2.5 in 2019.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2019]
    df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
    df = df.nlargest(9, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
        y=alt.Y('city:N', sort='-x', title='City'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
        tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 9 Cities by Median PM2.5 in 2019', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2019]
     df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
     df = df.nlargest(9, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
         y=alt.Y('city:N', sort='-x', title='City'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
         tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 9 Cities by Median PM2.5 in 2019', width=500, height=300)
     return chart


</code>
"
871,temporal_aggregation,Show the monthly average PM10 trend for Solapur from 2017 to 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Solapur'].copy()
    df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Solapur (2017–2022)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Solapur'].copy()
     df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Solapur (2017–2022)', width=600, height=300)
     return chart


</code>
"
872,spatio_temporal_aggregation,Show the monthly average PM2.5 for Haryana across each year from 2017 to 2024 as small multiples (faceted by year).,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Haryana'].copy()
    df['Year'] = df['Timestamp'].dt.year
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='steelblue').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5'),
        tooltip=['Year:O','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).facet(
        facet=alt.Facet('Year:O', title='Year'),
        columns=4
    ).properties(title='Monthly PM2.5 in Haryana by Year (2017–2024)')
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Haryana'].copy()
     df['Year'] = df['Timestamp'].dt.year
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='steelblue').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5'),
         tooltip=['Year:O','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).facet(
         facet=alt.Facet('Year:O', title='Year'),
         columns=4
     ).properties(title='Monthly PM2.5 in Haryana by Year (2017–2024)')
     return chart


</code>
"
873,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Karnataka stations in 2020, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Karnataka') & (data['Timestamp'].dt.year == 2020)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Karnataka Stations 2020', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Karnataka') & (data['Timestamp'].dt.year == 2020)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Karnataka Stations 2020', width=450, height=350)


</code>
"
874,temporal_aggregation,Show the monthly average PM2.5 for Haveri in 2019 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Haveri') & (data['Timestamp'].dt.year == 2019)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Haveri 2019', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Haveri') & (data['Timestamp'].dt.year == 2019)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Haveri 2019', width=450, height=280)


</code>
"
875,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Maihar, Ernakulam, and Visakhapatnam in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Maihar', 'Ernakulam', 'Visakhapatnam']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Maihar vs Ernakulam vs Visakhapatnam – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Maihar', 'Ernakulam', 'Visakhapatnam']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Maihar vs Ernakulam vs Visakhapatnam – 2023', width=550, height=320)
     return chart


</code>
"
876,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Rajasthan, Himachal Pradesh, and Tripura from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Rajasthan', 'Himachal Pradesh', 'Tripura']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Rajasthan vs Himachal Pradesh vs Tripura', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Rajasthan', 'Himachal Pradesh', 'Tripura']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Rajasthan vs Himachal Pradesh vs Tripura', width=550, height=320)
     return chart


</code>
"
877,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Uttar Pradesh stations in 2024, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Uttar Pradesh') & (data['Timestamp'].dt.year == 2024)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Uttar Pradesh Stations 2024', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Uttar Pradesh') & (data['Timestamp'].dt.year == 2024)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Uttar Pradesh Stations 2024', width=450, height=350)


</code>
"
878,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Byrnihat, Saharsa, and Khurja in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Byrnihat', 'Saharsa', 'Khurja']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Byrnihat vs Saharsa vs Khurja – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Byrnihat', 'Saharsa', 'Khurja']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Byrnihat vs Saharsa vs Khurja – 2022', width=550, height=320)
     return chart


</code>
"
879,spatial_aggregation,Show a bar chart of the top 8 cities by median PM2.5 in 2020.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2020]
    df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
    df = df.nlargest(8, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
        y=alt.Y('city:N', sort='-x', title='City'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
        tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 8 Cities by Median PM2.5 in 2020', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2020]
     df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
     df = df.nlargest(8, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
         y=alt.Y('city:N', sort='-x', title='City'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
         tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 8 Cities by Median PM2.5 in 2020', width=500, height=300)
     return chart


</code>
"
880,specific_pattern,Plot the rolling 30-day average PM2.5 for Manipur in 2020 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Manipur') & (data['Timestamp'].dt.year == 2020)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Manipur 2020', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Manipur') & (data['Timestamp'].dt.year == 2020)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Manipur 2020', width=600, height=300)


</code>
"
881,temporal_aggregation,Show the monthly average PM10 trend for Mandikhera from 2017 to 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Mandikhera'].copy()
    df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Mandikhera (2017–2022)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Mandikhera'].copy()
     df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Mandikhera (2017–2022)', width=600, height=300)
     return chart


</code>
"
882,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Uttar Pradesh stations in 2021, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Uttar Pradesh') & (data['Timestamp'].dt.year == 2021)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Uttar Pradesh Stations 2021', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Uttar Pradesh') & (data['Timestamp'].dt.year == 2021)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Uttar Pradesh Stations 2021', width=450, height=350)


</code>
"
883,spatio_temporal_aggregation,Create a heatmap showing the average PM2.5 by month (x-axis) and year (y-axis) for Odisha.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Odisha'].copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_rect().encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Year:N', title='Year'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='Avg PM2.5'),
        tooltip=['Year:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Heatmap – Odisha (Month × Year)', width=500, height=280)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Odisha'].copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_rect().encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Year:N', title='Year'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='Avg PM2.5'),
         tooltip=['Year:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Heatmap – Odisha (Month × Year)', width=500, height=280)
     return chart


</code>
"
884,temporal_aggregation,Plot the weekly average PM2.5 for Dharwad in 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Dharwad') & (data['Timestamp'].dt.year == 2024)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Dharwad 2024', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Dharwad') & (data['Timestamp'].dt.year == 2024)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Dharwad 2024', width=600, height=300)
     return chart


</code>
"
885,temporal_aggregation,Show a monthly bar chart of the number of days Tamil Nadu exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2021.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Tamil Nadu') & (data['Timestamp'].dt.year == 2021)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Tamil Nadu Exceeded WHO PM2.5 Guideline per Month – 2021', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Tamil Nadu') & (data['Timestamp'].dt.year == 2021)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Tamil Nadu Exceeded WHO PM2.5 Guideline per Month – 2021', width=500, height=300)
     return chart


</code>
"
886,spatial_aggregation,Plot the distribution of PM2.5 values in Kerala across all years using a histogram.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Kerala'].dropna(subset=['PM2.5'])
    df = df[['PM2.5']]
    chart = alt.Chart(df).mark_bar(color='steelblue', opacity=0.75).encode(
        x=alt.X('PM2\.5:Q', bin=alt.Bin(maxbins=40), title='PM2.5 (µg/m³)'),
        y=alt.Y('count()', title='Number of Observations'),
        tooltip=[alt.Tooltip('PM2\.5:Q', bin=True), 'count()']
    ).properties(title='PM2.5 Distribution – Kerala (All Years)', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Kerala'].dropna(subset=['PM2.5'])
     df = df[['PM2.5']]
     chart = alt.Chart(df).mark_bar(color='steelblue', opacity=0.75).encode(
         x=alt.X('PM2\.5:Q', bin=alt.Bin(maxbins=40), title='PM2.5 (µg/m³)'),
         y=alt.Y('count()', title='Number of Observations'),
         tooltip=[alt.Tooltip('PM2\.5:Q', bin=True), 'count()']
     ).properties(title='PM2.5 Distribution – Kerala (All Years)', width=500, height=300)
     return chart


</code>
"
887,temporal_aggregation,Show the monthly average PM2.5 for Nagaur in 2023 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Nagaur') & (data['Timestamp'].dt.year == 2023)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Nagaur 2023', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Nagaur') & (data['Timestamp'].dt.year == 2023)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Nagaur 2023', width=450, height=280)


</code>
"
888,spatial_aggregation,Plot the top 13 states by average PM2.5 in 2019 as a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2019]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nlargest(13, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 13 States by Average PM2.5 in 2019', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2019]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nlargest(13, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 13 States by Average PM2.5 in 2019', width=500, height=300)
     return chart


</code>
"
889,temporal_aggregation,Show the monthly average PM2.5 for Ahmedabad in 2017 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Ahmedabad') & (data['Timestamp'].dt.year == 2017)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Ahmedabad 2017', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Ahmedabad') & (data['Timestamp'].dt.year == 2017)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Ahmedabad 2017', width=450, height=280)


</code>
"
890,spatial_aggregation,Plot the distribution of PM2.5 values in Rajasthan across all years using a histogram.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Rajasthan'].dropna(subset=['PM2.5'])
    df = df[['PM2.5']]
    chart = alt.Chart(df).mark_bar(color='steelblue', opacity=0.75).encode(
        x=alt.X('PM2\.5:Q', bin=alt.Bin(maxbins=40), title='PM2.5 (µg/m³)'),
        y=alt.Y('count()', title='Number of Observations'),
        tooltip=[alt.Tooltip('PM2\.5:Q', bin=True), 'count()']
    ).properties(title='PM2.5 Distribution – Rajasthan (All Years)', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Rajasthan'].dropna(subset=['PM2.5'])
     df = df[['PM2.5']]
     chart = alt.Chart(df).mark_bar(color='steelblue', opacity=0.75).encode(
         x=alt.X('PM2\.5:Q', bin=alt.Bin(maxbins=40), title='PM2.5 (µg/m³)'),
         y=alt.Y('count()', title='Number of Observations'),
         tooltip=[alt.Tooltip('PM2\.5:Q', bin=True), 'count()']
     ).properties(title='PM2.5 Distribution – Rajasthan (All Years)', width=500, height=300)
     return chart


</code>
"
891,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Jharkhand, Jharkhand, and Jharkhand in 2018 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Jharkhand', 'Jharkhand', 'Jharkhand']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2018)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Jharkhand, Jharkhand, UP – 2018', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Jharkhand', 'Jharkhand', 'Jharkhand']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2018)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Jharkhand, Jharkhand, UP – 2018', width=550, height=320)
     return chart


</code>
"
892,spatio_temporal_aggregation,"Create a faceted bar chart showing top 6 states by average PM2.5 per year for 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year.isin([2019,2020,2021,2022])].copy()
    df['Year'] = df['Timestamp'].dt.year
    agg = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    top = agg.groupby('Year').apply(lambda x: x.nlargest(6,'PM2.5')).reset_index(drop=True)
    chart = alt.Chart(top).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Avg PM2.5'),
        y=alt.Y('state:N', sort='-x'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).facet(facet='Year:O', columns=2).properties(title='Top 6 States by PM2.5 per Year')
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year.isin([2019,2020,2021,2022])].copy()
     df['Year'] = df['Timestamp'].dt.year
     agg = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     top = agg.groupby('Year').apply(lambda x: x.nlargest(6,'PM2.5')).reset_index(drop=True)
     chart = alt.Chart(top).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Avg PM2.5'),
         y=alt.Y('state:N', sort='-x'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).facet(facet='Year:O', columns=2).properties(title='Top 6 States by PM2.5 per Year')
     return chart


</code>
"
893,temporal_aggregation,Show a monthly bar chart of the number of days Telangana exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2017.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Telangana') & (data['Timestamp'].dt.year == 2017)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Telangana Exceeded WHO PM2.5 Guideline per Month – 2017', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Telangana') & (data['Timestamp'].dt.year == 2017)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Telangana Exceeded WHO PM2.5 Guideline per Month – 2017', width=500, height=300)
     return chart


</code>
"
894,specific_pattern,Plot the rolling 30-day average PM2.5 for Jammu and Kashmir in 2018 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Jammu and Kashmir') & (data['Timestamp'].dt.year == 2018)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Jammu and Kashmir 2018', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Jammu and Kashmir') & (data['Timestamp'].dt.year == 2018)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Jammu and Kashmir 2018', width=600, height=300)


</code>
"
895,temporal_aggregation,Show the monthly average PM2.5 for Palwal  in 2018 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Palwal ') & (data['Timestamp'].dt.year == 2018)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Palwal  2018', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Palwal ') & (data['Timestamp'].dt.year == 2018)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Palwal  2018', width=450, height=280)


</code>
"
896,temporal_aggregation,Show the monthly average PM10 trend for Amritsar from 2017 to 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Amritsar'].copy()
    df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Amritsar (2017–2022)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Amritsar'].copy()
     df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Amritsar (2017–2022)', width=600, height=300)
     return chart


</code>
"
897,spatial_aggregation,Visualize the bottom 15 states with the lowest average PM2.5 in 2020 using a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2020]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nsmallest(15, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Bottom 15 States by Average PM2.5 in 2020', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2020]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nsmallest(15, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Bottom 15 States by Average PM2.5 in 2020', width=500, height=300)
     return chart


</code>
"
898,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Brajrajnagar, Bahadurgarh, and Tumidih in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Brajrajnagar', 'Bahadurgarh', 'Tumidih']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Brajrajnagar vs Bahadurgarh vs Tumidih – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Brajrajnagar', 'Bahadurgarh', 'Tumidih']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Brajrajnagar vs Bahadurgarh vs Tumidih – 2024', width=550, height=320)
     return chart


</code>
"
899,spatial_aggregation,"Show the top 6 states by average PM10 in 2017 as a bar chart, sorted in descending order.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2017]
    df = df.groupby('state')['PM10'].mean().reset_index().dropna()
    df = df.nlargest(6, 'PM10')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Top 6 States by Average PM10 in 2017', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2017]
     df = df.groupby('state')['PM10'].mean().reset_index().dropna()
     df = df.nlargest(6, 'PM10')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Top 6 States by Average PM10 in 2017', width=500, height=300)
     return chart


</code>
"
900,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Aizawl, Gurugram, and Darbhanga in 2020 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Aizawl', 'Gurugram', 'Darbhanga']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2020)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Aizawl vs Gurugram vs Darbhanga – 2020', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Aizawl', 'Gurugram', 'Darbhanga']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2020)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Aizawl vs Gurugram vs Darbhanga – 2020', width=550, height=320)
     return chart


</code>
"
901,temporal_aggregation,Show the monthly average PM2.5 for Gorakhpur in 2022 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Gorakhpur') & (data['Timestamp'].dt.year == 2022)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Gorakhpur 2022', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Gorakhpur') & (data['Timestamp'].dt.year == 2022)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Gorakhpur 2022', width=450, height=280)


</code>
"
902,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Motihari, Greater Noida, and Tiruchirappalli in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Motihari', 'Greater Noida', 'Tiruchirappalli']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Motihari vs Greater Noida vs Tiruchirappalli – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Motihari', 'Greater Noida', 'Tiruchirappalli']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Motihari vs Greater Noida vs Tiruchirappalli – 2024', width=550, height=320)
     return chart


</code>
"
903,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Chhattisgarh stations in 2019, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Chhattisgarh') & (data['Timestamp'].dt.year == 2019)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Chhattisgarh Stations 2019', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Chhattisgarh') & (data['Timestamp'].dt.year == 2019)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Chhattisgarh Stations 2019', width=450, height=350)


</code>
"
904,spatial_aggregation,Visualize the bottom 12 states with the lowest average PM2.5 in 2022 using a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2022]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nsmallest(12, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Bottom 12 States by Average PM2.5 in 2022', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2022]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nsmallest(12, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Bottom 12 States by Average PM2.5 in 2022', width=500, height=300)
     return chart


</code>
"
905,temporal_aggregation,Plot the weekly average PM2.5 for Sikar in 2023 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Sikar') & (data['Timestamp'].dt.year == 2023)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Sikar 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Sikar') & (data['Timestamp'].dt.year == 2023)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Sikar 2023', width=600, height=300)
     return chart


</code>
"
906,spatial_aggregation,Plot the top 15 states by average PM2.5 in 2017 as a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2017]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nlargest(15, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 15 States by Average PM2.5 in 2017', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2017]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nlargest(15, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 15 States by Average PM2.5 in 2017', width=500, height=300)
     return chart


</code>
"
907,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Madhya Pradesh, Haryana, and Himachal Pradesh from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Madhya Pradesh', 'Haryana', 'Himachal Pradesh']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Madhya Pradesh vs Haryana vs Himachal Pradesh', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Madhya Pradesh', 'Haryana', 'Himachal Pradesh']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Madhya Pradesh vs Haryana vs Himachal Pradesh', width=550, height=320)
     return chart


</code>
"
908,spatial_aggregation,Visualize the bottom 9 states with the lowest average PM2.5 in 2024 using a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2024]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nsmallest(9, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Bottom 9 States by Average PM2.5 in 2024', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2024]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nsmallest(9, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Bottom 9 States by Average PM2.5 in 2024', width=500, height=300)
     return chart


</code>
"
909,spatio_temporal_aggregation,Show a heatmap of average PM2.5 for the top 15 most polluted states by month for 2024.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    top8 = data.groupby('state')['PM2.5'].mean().nlargest(15).index.tolist()
    df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_rect().encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('state:N', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
        tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Heatmap – Top 15 Polluted States by Month (2024)', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     top8 = data.groupby('state')['PM2.5'].mean().nlargest(15).index.tolist()
     df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_rect().encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('state:N', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
         tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Heatmap – Top 15 Polluted States by Month (2024)', width=500, height=300)
     return chart


</code>
"
910,temporal_aggregation,Plot the monthly average PM2.5 trend for Bihar from 2017 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Bihar'].copy()
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='steelblue').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly Average PM2.5 Trend – Bihar (2017–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Bihar'].copy()
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='steelblue').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly Average PM2.5 Trend – Bihar (2017–2024)', width=600, height=300)
     return chart


</code>
"
911,spatial_aggregation,Visualize the bottom 7 states with the lowest average PM2.5 in 2017 using a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2017]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nsmallest(7, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Bottom 7 States by Average PM2.5 in 2017', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2017]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nsmallest(7, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Bottom 7 States by Average PM2.5 in 2017', width=500, height=300)
     return chart


</code>
"
912,temporal_aggregation,Show a monthly bar chart of the number of days Jharkhand exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2020.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Jharkhand') & (data['Timestamp'].dt.year == 2020)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Jharkhand Exceeded WHO PM2.5 Guideline per Month – 2020', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Jharkhand') & (data['Timestamp'].dt.year == 2020)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Jharkhand Exceeded WHO PM2.5 Guideline per Month – 2020', width=500, height=300)
     return chart


</code>
"
913,specific_pattern,Show a cumulative area chart of PM2.5 readings for Shillong across 2024.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Shillong') & (data['Timestamp'].dt.year == 2024)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Shillong 2024', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Shillong') & (data['Timestamp'].dt.year == 2024)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Shillong 2024', width=600, height=300)
     return chart


</code>
"
914,specific_pattern,Show a cumulative area chart of PM2.5 readings for Bareilly across 2023.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Bareilly') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Bareilly 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Bareilly') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Bareilly 2023', width=600, height=300)
     return chart


</code>
"
915,temporal_aggregation,Show the monthly average PM10 trend for Singrauli from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Singrauli'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Singrauli (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Singrauli'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Singrauli (2019–2024)', width=600, height=300)
     return chart


</code>
"
916,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Arunachal Pradesh stations in 2020, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Arunachal Pradesh') & (data['Timestamp'].dt.year == 2020)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Arunachal Pradesh Stations 2020', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Arunachal Pradesh') & (data['Timestamp'].dt.year == 2020)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Arunachal Pradesh Stations 2020', width=450, height=350)


</code>
"
917,spatial_aggregation,Show a bar chart of the top 15 cities by median PM2.5 in 2019.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2019]
    df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
    df = df.nlargest(15, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
        y=alt.Y('city:N', sort='-x', title='City'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
        tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 15 Cities by Median PM2.5 in 2019', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2019]
     df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
     df = df.nlargest(15, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
         y=alt.Y('city:N', sort='-x', title='City'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
         tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 15 Cities by Median PM2.5 in 2019', width=500, height=300)
     return chart


</code>
"
918,funding_based,Show a scatter plot of total NCAP funding vs average PM2.5 (2023) per state.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    pm = data[data['Timestamp'].dt.year == 2023].groupby('state')['PM2.5'].mean().reset_index()
    funding = ncap_funding_data.groupby('state')['Total fund released'].sum().reset_index()
    df = pm.merge(funding, on='state').dropna()
    chart = alt.Chart(df).mark_point(filled=True, size=100).encode(
        x=alt.X('Total fund released:Q', title='Total NCAP Funding (Cr)'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 – 2023 (µg/m³)'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f'),
                 alt.Tooltip('Total fund released:Q', format='.1f')]
    ).properties(title='NCAP Funding vs Average PM2.5 by State (2023)', width=450, height=350)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     pm = data[data['Timestamp'].dt.year == 2023].groupby('state')['PM2.5'].mean().reset_index()
     funding = ncap_funding_data.groupby('state')['Total fund released'].sum().reset_index()
     df = pm.merge(funding, on='state').dropna()
     chart = alt.Chart(df).mark_point(filled=True, size=100).encode(
         x=alt.X('Total fund released:Q', title='Total NCAP Funding (Cr)'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 – 2023 (µg/m³)'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f'),
                  alt.Tooltip('Total fund released:Q', format='.1f')]
     ).properties(title='NCAP Funding vs Average PM2.5 by State (2023)', width=450, height=350)
     return chart


</code>
"
919,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Puducherry, Puducherry, and Jharkhand across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Puducherry', 'Puducherry', 'Jharkhand']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Puducherry', 'Puducherry', 'Jharkhand']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
920,temporal_aggregation,Show a monthly bar chart of the number of days Tripura exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2022.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Tripura') & (data['Timestamp'].dt.year == 2022)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Tripura Exceeded WHO PM2.5 Guideline per Month – 2022', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Tripura') & (data['Timestamp'].dt.year == 2022)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Tripura Exceeded WHO PM2.5 Guideline per Month – 2022', width=500, height=300)
     return chart


</code>
"
921,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Arunachal Pradesh, Haryana, and Manipur in 2019 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Arunachal Pradesh', 'Haryana', 'Manipur']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Arunachal Pradesh, Haryana, UP – 2019', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Arunachal Pradesh', 'Haryana', 'Manipur']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Arunachal Pradesh, Haryana, UP – 2019', width=550, height=320)
     return chart


</code>
"
922,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Meghalaya, Karnataka, and Odisha from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Meghalaya', 'Karnataka', 'Odisha']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Meghalaya vs Karnataka vs Odisha', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Meghalaya', 'Karnataka', 'Odisha']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Meghalaya vs Karnataka vs Odisha', width=550, height=320)
     return chart


</code>
"
923,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Tamil Nadu, Madhya Pradesh, and Mizoram in 2017 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Tamil Nadu', 'Madhya Pradesh', 'Mizoram']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2017)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Tamil Nadu, Madhya Pradesh, UP – 2017', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Tamil Nadu', 'Madhya Pradesh', 'Mizoram']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2017)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Tamil Nadu, Madhya Pradesh, UP – 2017', width=550, height=320)
     return chart


</code>
"
924,spatial_aggregation,"Show the top 11 states by average PM10 in 2017 as a bar chart, sorted in descending order.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2017]
    df = df.groupby('state')['PM10'].mean().reset_index().dropna()
    df = df.nlargest(11, 'PM10')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Top 11 States by Average PM10 in 2017', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2017]
     df = df.groupby('state')['PM10'].mean().reset_index().dropna()
     df = df.nlargest(11, 'PM10')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Top 11 States by Average PM10 in 2017', width=500, height=300)
     return chart


</code>
"
925,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Meghalaya stations in 2017, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Meghalaya') & (data['Timestamp'].dt.year == 2017)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Meghalaya Stations 2017', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Meghalaya') & (data['Timestamp'].dt.year == 2017)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Meghalaya Stations 2017', width=450, height=350)


</code>
"
926,temporal_aggregation,Plot the weekly average PM2.5 for Rohtak in 2023 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Rohtak') & (data['Timestamp'].dt.year == 2023)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Rohtak 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Rohtak') & (data['Timestamp'].dt.year == 2023)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Rohtak 2023', width=600, height=300)
     return chart


</code>
"
927,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Tripura, Jammu and Kashmir, and Madhya Pradesh from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Tripura', 'Jammu and Kashmir', 'Madhya Pradesh']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Tripura vs Jammu and Kashmir vs Madhya Pradesh', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Tripura', 'Jammu and Kashmir', 'Madhya Pradesh']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Tripura vs Jammu and Kashmir vs Madhya Pradesh', width=550, height=320)
     return chart


</code>
"
928,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Andhra Pradesh, Jharkhand, and Jammu and Kashmir from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Andhra Pradesh', 'Jharkhand', 'Jammu and Kashmir']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Andhra Pradesh vs Jharkhand vs Jammu and Kashmir', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Andhra Pradesh', 'Jharkhand', 'Jammu and Kashmir']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Andhra Pradesh vs Jharkhand vs Jammu and Kashmir', width=550, height=320)
     return chart


</code>
"
929,spatio_temporal_aggregation,Show the monthly average PM2.5 for Meghalaya across each year from 2017 to 2024 as small multiples (faceted by year).,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Meghalaya'].copy()
    df['Year'] = df['Timestamp'].dt.year
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='steelblue').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5'),
        tooltip=['Year:O','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).facet(
        facet=alt.Facet('Year:O', title='Year'),
        columns=4
    ).properties(title='Monthly PM2.5 in Meghalaya by Year (2017–2024)')
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Meghalaya'].copy()
     df['Year'] = df['Timestamp'].dt.year
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='steelblue').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5'),
         tooltip=['Year:O','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).facet(
         facet=alt.Facet('Year:O', title='Year'),
         columns=4
     ).properties(title='Monthly PM2.5 in Meghalaya by Year (2017–2024)')
     return chart


</code>
"
930,temporal_aggregation,Show a monthly bar chart of the number of days Chandigarh exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2021.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Chandigarh') & (data['Timestamp'].dt.year == 2021)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Chandigarh Exceeded WHO PM2.5 Guideline per Month – 2021', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Chandigarh') & (data['Timestamp'].dt.year == 2021)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Chandigarh Exceeded WHO PM2.5 Guideline per Month – 2021', width=500, height=300)
     return chart


</code>
"
931,temporal_aggregation,Show the monthly average PM2.5 for Noida in 2017 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Noida') & (data['Timestamp'].dt.year == 2017)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Noida 2017', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Noida') & (data['Timestamp'].dt.year == 2017)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Noida 2017', width=450, height=280)


</code>
"
932,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Uttarakhand stations in 2017, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Uttarakhand') & (data['Timestamp'].dt.year == 2017)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Uttarakhand Stations 2017', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Uttarakhand') & (data['Timestamp'].dt.year == 2017)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Uttarakhand Stations 2017', width=450, height=350)


</code>
"
933,specific_pattern,Plot the rolling 30-day average PM2.5 for Bihar in 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Bihar') & (data['Timestamp'].dt.year == 2022)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Bihar 2022', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Bihar') & (data['Timestamp'].dt.year == 2022)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Bihar 2022', width=600, height=300)


</code>
"
934,specific_pattern,Show PM2.5 exceedances above the Indian standard (60 µg/m³) per year as a bar chart for Mizoram.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Mizoram'].dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 60].copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby('Year')['Timestamp'].nunique().reset_index()
    df.columns = ['Year','Days Exceeded']
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('Days Exceeded:Q', title='Days PM2.5 > 60 µg/m³'),
        tooltip=['Year:N','Days Exceeded:Q']
    ).properties(title='Days Mizoram Exceeded Indian PM2.5 Standard (60 µg/m³) per Year', width=450, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Mizoram'].dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 60].copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby('Year')['Timestamp'].nunique().reset_index()
     df.columns = ['Year','Days Exceeded']
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('Days Exceeded:Q', title='Days PM2.5 > 60 µg/m³'),
         tooltip=['Year:N','Days Exceeded:Q']
     ).properties(title='Days Mizoram Exceeded Indian PM2.5 Standard (60 µg/m³) per Year', width=450, height=300)
     return chart


</code>
"
935,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Uttarakhand, Sikkim, and Manipur across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Uttarakhand', 'Sikkim', 'Manipur']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Uttarakhand', 'Sikkim', 'Manipur']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
936,temporal_aggregation,Show the monthly average PM10 trend for Nashik from 2017 to 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Nashik'].copy()
    df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Nashik (2017–2022)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Nashik'].copy()
     df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Nashik (2017–2022)', width=600, height=300)
     return chart


</code>
"
937,specific_pattern,Show PM2.5 exceedances above the Indian standard (60 µg/m³) per year as a bar chart for Nagaland.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Nagaland'].dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 60].copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby('Year')['Timestamp'].nunique().reset_index()
    df.columns = ['Year','Days Exceeded']
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('Days Exceeded:Q', title='Days PM2.5 > 60 µg/m³'),
        tooltip=['Year:N','Days Exceeded:Q']
    ).properties(title='Days Nagaland Exceeded Indian PM2.5 Standard (60 µg/m³) per Year', width=450, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Nagaland'].dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 60].copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby('Year')['Timestamp'].nunique().reset_index()
     df.columns = ['Year','Days Exceeded']
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('Days Exceeded:Q', title='Days PM2.5 > 60 µg/m³'),
         tooltip=['Year:N','Days Exceeded:Q']
     ).properties(title='Days Nagaland Exceeded Indian PM2.5 Standard (60 µg/m³) per Year', width=450, height=300)
     return chart


</code>
"
938,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Uttarakhand stations in 2021, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Uttarakhand') & (data['Timestamp'].dt.year == 2021)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Uttarakhand Stations 2021', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Uttarakhand') & (data['Timestamp'].dt.year == 2021)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Uttarakhand Stations 2021', width=450, height=350)


</code>
"
939,specific_pattern,Show a cumulative area chart of PM2.5 readings for Jalore across 2024.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Jalore') & (data['Timestamp'].dt.year == 2024)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Jalore 2024', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Jalore') & (data['Timestamp'].dt.year == 2024)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Jalore 2024', width=600, height=300)
     return chart


</code>
"
940,temporal_aggregation,Show the monthly average PM10 trend for Thane from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Thane'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Thane (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Thane'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Thane (2019–2024)', width=600, height=300)
     return chart


</code>
"
941,temporal_aggregation,Show the monthly average PM2.5 for Keonjhar in 2018 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Keonjhar') & (data['Timestamp'].dt.year == 2018)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Keonjhar 2018', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Keonjhar') & (data['Timestamp'].dt.year == 2018)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Keonjhar 2018', width=450, height=280)


</code>
"
942,specific_pattern,Plot the rolling 30-day average PM2.5 for Uttarakhand in 2019 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Uttarakhand') & (data['Timestamp'].dt.year == 2019)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Uttarakhand 2019', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Uttarakhand') & (data['Timestamp'].dt.year == 2019)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Uttarakhand 2019', width=600, height=300)


</code>
"
943,specific_pattern,Plot the rolling 30-day average PM2.5 for Punjab in 2018 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Punjab') & (data['Timestamp'].dt.year == 2018)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Punjab 2018', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Punjab') & (data['Timestamp'].dt.year == 2018)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Punjab 2018', width=600, height=300)


</code>
"
944,specific_pattern,Show a cumulative area chart of PM2.5 readings for Latur across 2023.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Latur') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Latur 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Latur') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Latur 2023', width=600, height=300)
     return chart


</code>
"
945,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Rajasthan stations in 2023, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Rajasthan') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Rajasthan Stations 2023', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Rajasthan') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Rajasthan Stations 2023', width=450, height=350)


</code>
"
946,spatial_aggregation,Plot the top 6 states by average PM2.5 in 2022 as a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2022]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nlargest(6, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 6 States by Average PM2.5 in 2022', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2022]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nlargest(6, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 6 States by Average PM2.5 in 2022', width=500, height=300)
     return chart


</code>
"
947,spatial_aggregation,"Show the top 7 states by average PM10 in 2021 as a bar chart, sorted in descending order.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2021]
    df = df.groupby('state')['PM10'].mean().reset_index().dropna()
    df = df.nlargest(7, 'PM10')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Top 7 States by Average PM10 in 2021', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2021]
     df = df.groupby('state')['PM10'].mean().reset_index().dropna()
     df = df.nlargest(7, 'PM10')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Top 7 States by Average PM10 in 2021', width=500, height=300)
     return chart


</code>
"
948,spatio_temporal_aggregation,Show the monthly average PM2.5 for Puducherry across each year from 2017 to 2024 as small multiples (faceted by year).,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Puducherry'].copy()
    df['Year'] = df['Timestamp'].dt.year
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='steelblue').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5'),
        tooltip=['Year:O','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).facet(
        facet=alt.Facet('Year:O', title='Year'),
        columns=4
    ).properties(title='Monthly PM2.5 in Puducherry by Year (2017–2024)')
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Puducherry'].copy()
     df['Year'] = df['Timestamp'].dt.year
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='steelblue').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5'),
         tooltip=['Year:O','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).facet(
         facet=alt.Facet('Year:O', title='Year'),
         columns=4
     ).properties(title='Monthly PM2.5 in Puducherry by Year (2017–2024)')
     return chart


</code>
"
949,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Rajasthan, Meghalaya, and Odisha from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Rajasthan', 'Meghalaya', 'Odisha']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Rajasthan vs Meghalaya vs Odisha', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Rajasthan', 'Meghalaya', 'Odisha']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Rajasthan vs Meghalaya vs Odisha', width=550, height=320)
     return chart


</code>
"
950,specific_pattern,Show a cumulative area chart of PM2.5 readings for Bahadurgarh across 2019.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Bahadurgarh') & (data['Timestamp'].dt.year == 2019)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Bahadurgarh 2019', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Bahadurgarh') & (data['Timestamp'].dt.year == 2019)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Bahadurgarh 2019', width=600, height=300)
     return chart


</code>
"
951,spatio_temporal_aggregation,Create a heatmap showing the average PM2.5 by month (x-axis) and year (y-axis) for West Bengal.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'West Bengal'].copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_rect().encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Year:N', title='Year'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='Avg PM2.5'),
        tooltip=['Year:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Heatmap – West Bengal (Month × Year)', width=500, height=280)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'West Bengal'].copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_rect().encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Year:N', title='Year'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='Avg PM2.5'),
         tooltip=['Year:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Heatmap – West Bengal (Month × Year)', width=500, height=280)
     return chart


</code>
"
952,spatial_aggregation,Plot the top 9 states by average PM2.5 in 2023 as a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2023]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nlargest(9, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 9 States by Average PM2.5 in 2023', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2023]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nlargest(9, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 9 States by Average PM2.5 in 2023', width=500, height=300)
     return chart


</code>
"
953,temporal_aggregation,Show the monthly average PM2.5 for Rajgir in 2022 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Rajgir') & (data['Timestamp'].dt.year == 2022)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Rajgir 2022', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Rajgir') & (data['Timestamp'].dt.year == 2022)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Rajgir 2022', width=450, height=280)


</code>
"
954,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Uttarakhand, Punjab, and Odisha from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Uttarakhand', 'Punjab', 'Odisha']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Uttarakhand vs Punjab vs Odisha', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Uttarakhand', 'Punjab', 'Odisha']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Uttarakhand vs Punjab vs Odisha', width=550, height=320)
     return chart


</code>
"
955,spatio_temporal_aggregation,Show a heatmap of average PM2.5 for the top 14 most polluted states by month for 2024.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    top8 = data.groupby('state')['PM2.5'].mean().nlargest(14).index.tolist()
    df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_rect().encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('state:N', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
        tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Heatmap – Top 14 Polluted States by Month (2024)', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     top8 = data.groupby('state')['PM2.5'].mean().nlargest(14).index.tolist()
     df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_rect().encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('state:N', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
         tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Heatmap – Top 14 Polluted States by Month (2024)', width=500, height=300)
     return chart


</code>
"
956,temporal_aggregation,Show the monthly average PM2.5 for Gangtok in 2024 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Gangtok') & (data['Timestamp'].dt.year == 2024)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Gangtok 2024', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Gangtok') & (data['Timestamp'].dt.year == 2024)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Gangtok 2024', width=450, height=280)


</code>
"
957,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Meghalaya, Chandigarh, and Haryana across 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Meghalaya', 'Chandigarh', 'Haryana']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Meghalaya', 'Chandigarh', 'Haryana']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
     return chart


</code>
"
958,spatial_aggregation,Show a bar chart of the top 10 cities by median PM2.5 in 2024.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2024]
    df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
    df = df.nlargest(10, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
        y=alt.Y('city:N', sort='-x', title='City'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
        tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 10 Cities by Median PM2.5 in 2024', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2024]
     df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
     df = df.nlargest(10, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
         y=alt.Y('city:N', sort='-x', title='City'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
         tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 10 Cities by Median PM2.5 in 2024', width=500, height=300)
     return chart


</code>
"
959,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Meghalaya stations in 2021, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Meghalaya') & (data['Timestamp'].dt.year == 2021)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Meghalaya Stations 2021', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Meghalaya') & (data['Timestamp'].dt.year == 2021)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Meghalaya Stations 2021', width=450, height=350)


</code>
"
960,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Uttar Pradesh, Puducherry, and Delhi from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Uttar Pradesh', 'Puducherry', 'Delhi']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Uttar Pradesh vs Puducherry vs Delhi', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Uttar Pradesh', 'Puducherry', 'Delhi']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Uttar Pradesh vs Puducherry vs Delhi', width=550, height=320)
     return chart


</code>
"
961,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Thiruvananthapuram, Anantapur, and Karwar in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Thiruvananthapuram', 'Anantapur', 'Karwar']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Thiruvananthapuram vs Anantapur vs Karwar – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Thiruvananthapuram', 'Anantapur', 'Karwar']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Thiruvananthapuram vs Anantapur vs Karwar – 2022', width=550, height=320)
     return chart


</code>
"
962,spatial_aggregation,Visualize the bottom 15 states with the lowest average PM2.5 in 2019 using a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2019]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nsmallest(15, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Bottom 15 States by Average PM2.5 in 2019', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2019]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nsmallest(15, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Bottom 15 States by Average PM2.5 in 2019', width=500, height=300)
     return chart


</code>
"
963,temporal_aggregation,Show the monthly average PM2.5 for Thanjavur in 2019 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Thanjavur') & (data['Timestamp'].dt.year == 2019)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Thanjavur 2019', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Thanjavur') & (data['Timestamp'].dt.year == 2019)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Thanjavur 2019', width=450, height=280)


</code>
"
964,temporal_aggregation,Show a monthly bar chart of the number of days Meghalaya exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2024.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Meghalaya') & (data['Timestamp'].dt.year == 2024)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Meghalaya Exceeded WHO PM2.5 Guideline per Month – 2024', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Meghalaya') & (data['Timestamp'].dt.year == 2024)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Meghalaya Exceeded WHO PM2.5 Guideline per Month – 2024', width=500, height=300)
     return chart


</code>
"
965,spatial_aggregation,Plot the distribution of PM2.5 values in Sikkim across all years using a histogram.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Sikkim'].dropna(subset=['PM2.5'])
    df = df[['PM2.5']]
    chart = alt.Chart(df).mark_bar(color='steelblue', opacity=0.75).encode(
        x=alt.X('PM2\.5:Q', bin=alt.Bin(maxbins=40), title='PM2.5 (µg/m³)'),
        y=alt.Y('count()', title='Number of Observations'),
        tooltip=[alt.Tooltip('PM2\.5:Q', bin=True), 'count()']
    ).properties(title='PM2.5 Distribution – Sikkim (All Years)', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Sikkim'].dropna(subset=['PM2.5'])
     df = df[['PM2.5']]
     chart = alt.Chart(df).mark_bar(color='steelblue', opacity=0.75).encode(
         x=alt.X('PM2\.5:Q', bin=alt.Bin(maxbins=40), title='PM2.5 (µg/m³)'),
         y=alt.Y('count()', title='Number of Observations'),
         tooltip=[alt.Tooltip('PM2\.5:Q', bin=True), 'count()']
     ).properties(title='PM2.5 Distribution – Sikkim (All Years)', width=500, height=300)
     return chart


</code>
"
966,temporal_aggregation,Plot the weekly average PM2.5 for Prayagraj in 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Prayagraj') & (data['Timestamp'].dt.year == 2024)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Prayagraj 2024', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Prayagraj') & (data['Timestamp'].dt.year == 2024)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Prayagraj 2024', width=600, height=300)
     return chart


</code>
"
967,spatial_aggregation,Visualize the bottom 6 states with the lowest average PM2.5 in 2017 using a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2017]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nsmallest(6, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Bottom 6 States by Average PM2.5 in 2017', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2017]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nsmallest(6, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Bottom 6 States by Average PM2.5 in 2017', width=500, height=300)
     return chart


</code>
"
968,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Jodhpur, Howrah, and Belapur in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Jodhpur', 'Howrah', 'Belapur']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Jodhpur vs Howrah vs Belapur – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Jodhpur', 'Howrah', 'Belapur']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Jodhpur vs Howrah vs Belapur – 2022', width=550, height=320)
     return chart


</code>
"
969,spatial_aggregation,Plot the distribution of PM2.5 values in Karnataka across all years using a histogram.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Karnataka'].dropna(subset=['PM2.5'])
    df = df[['PM2.5']]
    chart = alt.Chart(df).mark_bar(color='steelblue', opacity=0.75).encode(
        x=alt.X('PM2\.5:Q', bin=alt.Bin(maxbins=40), title='PM2.5 (µg/m³)'),
        y=alt.Y('count()', title='Number of Observations'),
        tooltip=[alt.Tooltip('PM2\.5:Q', bin=True), 'count()']
    ).properties(title='PM2.5 Distribution – Karnataka (All Years)', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Karnataka'].dropna(subset=['PM2.5'])
     df = df[['PM2.5']]
     chart = alt.Chart(df).mark_bar(color='steelblue', opacity=0.75).encode(
         x=alt.X('PM2\.5:Q', bin=alt.Bin(maxbins=40), title='PM2.5 (µg/m³)'),
         y=alt.Y('count()', title='Number of Observations'),
         tooltip=[alt.Tooltip('PM2\.5:Q', bin=True), 'count()']
     ).properties(title='PM2.5 Distribution – Karnataka (All Years)', width=500, height=300)
     return chart


</code>
"
970,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Andhra Pradesh, Puducherry, and Karnataka across 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Andhra Pradesh', 'Puducherry', 'Karnataka']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Andhra Pradesh', 'Puducherry', 'Karnataka']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
     return chart


</code>
"
971,spatial_aggregation,Show a bar chart of the top 14 cities by median PM2.5 in 2024.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2024]
    df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
    df = df.nlargest(14, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
        y=alt.Y('city:N', sort='-x', title='City'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
        tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 14 Cities by Median PM2.5 in 2024', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2024]
     df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
     df = df.nlargest(14, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
         y=alt.Y('city:N', sort='-x', title='City'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
         tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 14 Cities by Median PM2.5 in 2024', width=500, height=300)
     return chart


</code>
"
972,spatial_aggregation,Visualize the bottom 6 states with the lowest average PM2.5 in 2023 using a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2023]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nsmallest(6, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Bottom 6 States by Average PM2.5 in 2023', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2023]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nsmallest(6, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Bottom 6 States by Average PM2.5 in 2023', width=500, height=300)
     return chart


</code>
"
973,spatio_temporal_aggregation,Show the monthly average PM2.5 for Tamil Nadu across each year from 2017 to 2024 as small multiples (faceted by year).,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Tamil Nadu'].copy()
    df['Year'] = df['Timestamp'].dt.year
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='steelblue').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5'),
        tooltip=['Year:O','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).facet(
        facet=alt.Facet('Year:O', title='Year'),
        columns=4
    ).properties(title='Monthly PM2.5 in Tamil Nadu by Year (2017–2024)')
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Tamil Nadu'].copy()
     df['Year'] = df['Timestamp'].dt.year
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='steelblue').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5'),
         tooltip=['Year:O','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).facet(
         facet=alt.Facet('Year:O', title='Year'),
         columns=4
     ).properties(title='Monthly PM2.5 in Tamil Nadu by Year (2017–2024)')
     return chart


</code>
"
974,specific_pattern,Plot the rolling 30-day average PM2.5 for Nagaland in 2019 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Nagaland') & (data['Timestamp'].dt.year == 2019)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Nagaland 2019', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Nagaland') & (data['Timestamp'].dt.year == 2019)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Nagaland 2019', width=600, height=300)


</code>
"
975,spatio_temporal_aggregation,"Create a faceted bar chart showing top 5 states by average PM2.5 per year for 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year.isin([2020,2021,2022,2023])].copy()
    df['Year'] = df['Timestamp'].dt.year
    agg = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    top = agg.groupby('Year').apply(lambda x: x.nlargest(5,'PM2.5')).reset_index(drop=True)
    chart = alt.Chart(top).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Avg PM2.5'),
        y=alt.Y('state:N', sort='-x'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).facet(facet='Year:O', columns=2).properties(title='Top 5 States by PM2.5 per Year')
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year.isin([2020,2021,2022,2023])].copy()
     df['Year'] = df['Timestamp'].dt.year
     agg = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     top = agg.groupby('Year').apply(lambda x: x.nlargest(5,'PM2.5')).reset_index(drop=True)
     chart = alt.Chart(top).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Avg PM2.5'),
         y=alt.Y('state:N', sort='-x'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).facet(facet='Year:O', columns=2).properties(title='Top 5 States by PM2.5 per Year')
     return chart


</code>
"
976,spatial_aggregation,Plot the top 14 states by average PM2.5 in 2022 as a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2022]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nlargest(14, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 14 States by Average PM2.5 in 2022', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2022]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nlargest(14, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 14 States by Average PM2.5 in 2022', width=500, height=300)
     return chart


</code>
"
977,spatio_temporal_aggregation,"Create a faceted bar chart showing top 10 states by average PM2.5 per year for 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year.isin([2019,2020,2021,2022])].copy()
    df['Year'] = df['Timestamp'].dt.year
    agg = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    top = agg.groupby('Year').apply(lambda x: x.nlargest(10,'PM2.5')).reset_index(drop=True)
    chart = alt.Chart(top).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Avg PM2.5'),
        y=alt.Y('state:N', sort='-x'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).facet(facet='Year:O', columns=2).properties(title='Top 10 States by PM2.5 per Year')
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year.isin([2019,2020,2021,2022])].copy()
     df['Year'] = df['Timestamp'].dt.year
     agg = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     top = agg.groupby('Year').apply(lambda x: x.nlargest(10,'PM2.5')).reset_index(drop=True)
     chart = alt.Chart(top).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Avg PM2.5'),
         y=alt.Y('state:N', sort='-x'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).facet(facet='Year:O', columns=2).properties(title='Top 10 States by PM2.5 per Year')
     return chart


</code>
"
978,temporal_aggregation,Show a monthly bar chart of the number of days Gujarat exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2018.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Gujarat') & (data['Timestamp'].dt.year == 2018)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Gujarat Exceeded WHO PM2.5 Guideline per Month – 2018', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Gujarat') & (data['Timestamp'].dt.year == 2018)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Gujarat Exceeded WHO PM2.5 Guideline per Month – 2018', width=500, height=300)
     return chart


</code>
"
979,temporal_aggregation,Show the monthly average PM2.5 for Singrauli in 2023 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Singrauli') & (data['Timestamp'].dt.year == 2023)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Singrauli 2023', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Singrauli') & (data['Timestamp'].dt.year == 2023)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Singrauli 2023', width=450, height=280)


</code>
"
980,spatio_temporal_aggregation,Show the monthly average PM2.5 for Nagaland across each year from 2017 to 2024 as small multiples (faceted by year).,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Nagaland'].copy()
    df['Year'] = df['Timestamp'].dt.year
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='steelblue').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5'),
        tooltip=['Year:O','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).facet(
        facet=alt.Facet('Year:O', title='Year'),
        columns=4
    ).properties(title='Monthly PM2.5 in Nagaland by Year (2017–2024)')
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Nagaland'].copy()
     df['Year'] = df['Timestamp'].dt.year
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='steelblue').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5'),
         tooltip=['Year:O','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).facet(
         facet=alt.Facet('Year:O', title='Year'),
         columns=4
     ).properties(title='Monthly PM2.5 in Nagaland by Year (2017–2024)')
     return chart


</code>
"
981,specific_pattern,Show a cumulative area chart of PM2.5 readings for Darbhanga across 2022.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Darbhanga') & (data['Timestamp'].dt.year == 2022)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Darbhanga 2022', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Darbhanga') & (data['Timestamp'].dt.year == 2022)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Darbhanga 2022', width=600, height=300)
     return chart


</code>
"
982,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Andhra Pradesh, Gujarat, and Jammu and Kashmir across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Andhra Pradesh', 'Gujarat', 'Jammu and Kashmir']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Andhra Pradesh', 'Gujarat', 'Jammu and Kashmir']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
983,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Punjab, Odisha, and Rajasthan from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Punjab', 'Odisha', 'Rajasthan']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Punjab vs Odisha vs Rajasthan', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Punjab', 'Odisha', 'Rajasthan']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Punjab vs Odisha vs Rajasthan', width=550, height=320)
     return chart


</code>
"
984,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Telangana, Chandigarh, and Jharkhand across 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Telangana', 'Chandigarh', 'Jharkhand']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Telangana', 'Chandigarh', 'Jharkhand']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
     return chart


</code>
"
985,specific_pattern,Plot the rolling 30-day average PM2.5 for Jammu and Kashmir in 2023 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Jammu and Kashmir') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Jammu and Kashmir 2023', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Jammu and Kashmir') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Jammu and Kashmir 2023', width=600, height=300)


</code>
"
986,spatial_aggregation,Show a bar chart of the top 13 cities by median PM2.5 in 2017.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2017]
    df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
    df = df.nlargest(13, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
        y=alt.Y('city:N', sort='-x', title='City'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
        tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 13 Cities by Median PM2.5 in 2017', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2017]
     df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
     df = df.nlargest(13, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
         y=alt.Y('city:N', sort='-x', title='City'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
         tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 13 Cities by Median PM2.5 in 2017', width=500, height=300)
     return chart


</code>
"
987,spatial_aggregation,Visualize the bottom 9 states with the lowest average PM2.5 in 2022 using a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2022]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nsmallest(9, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Bottom 9 States by Average PM2.5 in 2022', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2022]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nsmallest(9, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Bottom 9 States by Average PM2.5 in 2022', width=500, height=300)
     return chart


</code>
"
988,spatio_temporal_aggregation,Show a heatmap of average PM2.5 for the top 8 most polluted states by month for 2021.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    top8 = data.groupby('state')['PM2.5'].mean().nlargest(8).index.tolist()
    df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2021)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_rect().encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('state:N', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
        tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Heatmap – Top 8 Polluted States by Month (2021)', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     top8 = data.groupby('state')['PM2.5'].mean().nlargest(8).index.tolist()
     df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2021)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_rect().encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('state:N', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
         tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Heatmap – Top 8 Polluted States by Month (2021)', width=500, height=300)
     return chart


</code>
"
989,spatio_temporal_aggregation,Show the monthly average PM2.5 for Chandigarh across each year from 2017 to 2024 as small multiples (faceted by year).,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Chandigarh'].copy()
    df['Year'] = df['Timestamp'].dt.year
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='steelblue').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5'),
        tooltip=['Year:O','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).facet(
        facet=alt.Facet('Year:O', title='Year'),
        columns=4
    ).properties(title='Monthly PM2.5 in Chandigarh by Year (2017–2024)')
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Chandigarh'].copy()
     df['Year'] = df['Timestamp'].dt.year
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='steelblue').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5'),
         tooltip=['Year:O','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).facet(
         facet=alt.Facet('Year:O', title='Year'),
         columns=4
     ).properties(title='Monthly PM2.5 in Chandigarh by Year (2017–2024)')
     return chart


</code>
"
990,temporal_aggregation,Show a monthly bar chart of the number of days Manipur exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2024.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Manipur') & (data['Timestamp'].dt.year == 2024)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Manipur Exceeded WHO PM2.5 Guideline per Month – 2024', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Manipur') & (data['Timestamp'].dt.year == 2024)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Manipur Exceeded WHO PM2.5 Guideline per Month – 2024', width=500, height=300)
     return chart


</code>
"
991,temporal_aggregation,Show the monthly average PM10 trend for Dehradun from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Dehradun'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Dehradun (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Dehradun'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Dehradun (2019–2024)', width=600, height=300)
     return chart


</code>
"
992,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Assam, Jharkhand, and Haryana from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Assam', 'Jharkhand', 'Haryana']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Assam vs Jharkhand vs Haryana', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Assam', 'Jharkhand', 'Haryana']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Assam vs Jharkhand vs Haryana', width=550, height=320)
     return chart


</code>
"
993,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Arunachal Pradesh, Delhi, and Puducherry from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Arunachal Pradesh', 'Delhi', 'Puducherry']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Arunachal Pradesh vs Delhi vs Puducherry', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Arunachal Pradesh', 'Delhi', 'Puducherry']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Arunachal Pradesh vs Delhi vs Puducherry', width=550, height=320)
     return chart


</code>
"
994,spatio_temporal_aggregation,Show a heatmap of average PM2.5 for the top 12 most polluted states by month for 2021.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    top8 = data.groupby('state')['PM2.5'].mean().nlargest(12).index.tolist()
    df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2021)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_rect().encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('state:N', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
        tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Heatmap – Top 12 Polluted States by Month (2021)', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     top8 = data.groupby('state')['PM2.5'].mean().nlargest(12).index.tolist()
     df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2021)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_rect().encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('state:N', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
         tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Heatmap – Top 12 Polluted States by Month (2021)', width=500, height=300)
     return chart


</code>
"
995,spatial_aggregation,Show a bar chart of the top 9 cities by median PM2.5 in 2021.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2021]
    df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
    df = df.nlargest(9, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
        y=alt.Y('city:N', sort='-x', title='City'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
        tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 9 Cities by Median PM2.5 in 2021', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2021]
     df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
     df = df.nlargest(9, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
         y=alt.Y('city:N', sort='-x', title='City'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
         tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 9 Cities by Median PM2.5 in 2021', width=500, height=300)
     return chart


</code>
"
996,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Assam, Odisha, and Manipur from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Assam', 'Odisha', 'Manipur']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Assam vs Odisha vs Manipur', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Assam', 'Odisha', 'Manipur']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Assam vs Odisha vs Manipur', width=550, height=320)
     return chart


</code>
"
997,temporal_aggregation,Show a monthly bar chart of the number of days Nagaland exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2020.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Nagaland') & (data['Timestamp'].dt.year == 2020)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Nagaland Exceeded WHO PM2.5 Guideline per Month – 2020', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Nagaland') & (data['Timestamp'].dt.year == 2020)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Nagaland Exceeded WHO PM2.5 Guideline per Month – 2020', width=500, height=300)
     return chart


</code>
"
998,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Tamil Nadu, Jammu and Kashmir, and Tamil Nadu from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Tamil Nadu', 'Jammu and Kashmir', 'Tamil Nadu']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Tamil Nadu vs Jammu and Kashmir vs Tamil Nadu', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Tamil Nadu', 'Jammu and Kashmir', 'Tamil Nadu']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Tamil Nadu vs Jammu and Kashmir vs Tamil Nadu', width=550, height=320)
     return chart


</code>
"
999,temporal_aggregation,Plot the monthly average PM2.5 trend for Maharashtra from 2017 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Maharashtra'].copy()
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='steelblue').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly Average PM2.5 Trend – Maharashtra (2017–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Maharashtra'].copy()
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='steelblue').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly Average PM2.5 Trend – Maharashtra (2017–2024)', width=600, height=300)
     return chart


</code>
"
1000,specific_pattern,Plot the rolling 30-day average PM2.5 for Maharashtra in 2020 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Maharashtra') & (data['Timestamp'].dt.year == 2020)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Maharashtra 2020', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Maharashtra') & (data['Timestamp'].dt.year == 2020)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Maharashtra 2020', width=600, height=300)


</code>
"
1001,temporal_aggregation,Plot the weekly average PM2.5 for Delhi in 2021 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Delhi') & (data['Timestamp'].dt.year == 2021)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Delhi 2021', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Delhi') & (data['Timestamp'].dt.year == 2021)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Delhi 2021', width=600, height=300)
     return chart


</code>
"
1002,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Gujarat stations in 2017, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Gujarat') & (data['Timestamp'].dt.year == 2017)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Gujarat Stations 2017', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Gujarat') & (data['Timestamp'].dt.year == 2017)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Gujarat Stations 2017', width=450, height=350)


</code>
"
1003,spatial_aggregation,Visualize the bottom 14 states with the lowest average PM2.5 in 2018 using a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2018]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nsmallest(14, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Bottom 14 States by Average PM2.5 in 2018', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2018]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nsmallest(14, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Bottom 14 States by Average PM2.5 in 2018', width=500, height=300)
     return chart


</code>
"
1004,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Yadgir, Panchkula, and Thiruvananthapuram in 2018 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Yadgir', 'Panchkula', 'Thiruvananthapuram']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2018)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Yadgir vs Panchkula vs Thiruvananthapuram – 2018', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Yadgir', 'Panchkula', 'Thiruvananthapuram']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2018)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Yadgir vs Panchkula vs Thiruvananthapuram – 2018', width=550, height=320)
     return chart


</code>
"
1005,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Telangana, Nagaland, and Kerala from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Telangana', 'Nagaland', 'Kerala']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Telangana vs Nagaland vs Kerala', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Telangana', 'Nagaland', 'Kerala']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Telangana vs Nagaland vs Kerala', width=550, height=320)
     return chart


</code>
"
1006,temporal_aggregation,Show a monthly bar chart of the number of days Puducherry exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2023.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Puducherry') & (data['Timestamp'].dt.year == 2023)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Puducherry Exceeded WHO PM2.5 Guideline per Month – 2023', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Puducherry') & (data['Timestamp'].dt.year == 2023)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Puducherry Exceeded WHO PM2.5 Guideline per Month – 2023', width=500, height=300)
     return chart


</code>
"
1007,temporal_aggregation,Show the monthly average PM2.5 for Suakati in 2022 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Suakati') & (data['Timestamp'].dt.year == 2022)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Suakati 2022', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Suakati') & (data['Timestamp'].dt.year == 2022)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Suakati 2022', width=450, height=280)


</code>
"
1008,specific_pattern,Plot the rolling 30-day average PM2.5 for Telangana in 2020 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Telangana') & (data['Timestamp'].dt.year == 2020)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Telangana 2020', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Telangana') & (data['Timestamp'].dt.year == 2020)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Telangana 2020', width=600, height=300)


</code>
"
1009,specific_pattern,Show PM2.5 exceedances above the Indian standard (60 µg/m³) per year as a bar chart for Chandigarh.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Chandigarh'].dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 60].copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby('Year')['Timestamp'].nunique().reset_index()
    df.columns = ['Year','Days Exceeded']
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('Days Exceeded:Q', title='Days PM2.5 > 60 µg/m³'),
        tooltip=['Year:N','Days Exceeded:Q']
    ).properties(title='Days Chandigarh Exceeded Indian PM2.5 Standard (60 µg/m³) per Year', width=450, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Chandigarh'].dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 60].copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby('Year')['Timestamp'].nunique().reset_index()
     df.columns = ['Year','Days Exceeded']
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('Days Exceeded:Q', title='Days PM2.5 > 60 µg/m³'),
         tooltip=['Year:N','Days Exceeded:Q']
     ).properties(title='Days Chandigarh Exceeded Indian PM2.5 Standard (60 µg/m³) per Year', width=450, height=300)
     return chart


</code>
"
1010,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Jharkhand, Arunachal Pradesh, and Tripura across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Jharkhand', 'Arunachal Pradesh', 'Tripura']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Jharkhand', 'Arunachal Pradesh', 'Tripura']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
1011,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for West Bengal, Maharashtra, and Tamil Nadu from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['West Bengal', 'Maharashtra', 'Tamil Nadu']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: West Bengal vs Maharashtra vs Tamil Nadu', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['West Bengal', 'Maharashtra', 'Tamil Nadu']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: West Bengal vs Maharashtra vs Tamil Nadu', width=550, height=320)
     return chart


</code>
"
1012,specific_pattern,Show a cumulative area chart of PM2.5 readings for Bihar Sharif across 2022.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Bihar Sharif') & (data['Timestamp'].dt.year == 2022)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Bihar Sharif 2022', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Bihar Sharif') & (data['Timestamp'].dt.year == 2022)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Bihar Sharif 2022', width=600, height=300)
     return chart


</code>
"
1013,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Haryana, Sikkim, and Sikkim across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Haryana', 'Sikkim', 'Sikkim']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Haryana', 'Sikkim', 'Sikkim']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
1014,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Uttarakhand, Assam, and Tripura from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Uttarakhand', 'Assam', 'Tripura']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Uttarakhand vs Assam vs Tripura', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Uttarakhand', 'Assam', 'Tripura']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Uttarakhand vs Assam vs Tripura', width=550, height=320)
     return chart


</code>
"
1015,temporal_aggregation,Show the monthly average PM2.5 for Ahmednagar in 2017 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Ahmednagar') & (data['Timestamp'].dt.year == 2017)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Ahmednagar 2017', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Ahmednagar') & (data['Timestamp'].dt.year == 2017)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Ahmednagar 2017', width=450, height=280)


</code>
"
1016,spatial_aggregation,Plot the top 15 states by average PM2.5 in 2021 as a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2021]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nlargest(15, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 15 States by Average PM2.5 in 2021', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2021]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nlargest(15, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 15 States by Average PM2.5 in 2021', width=500, height=300)
     return chart


</code>
"
1017,temporal_aggregation,Plot the weekly average PM2.5 for Kozhikode in 2023 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Kozhikode') & (data['Timestamp'].dt.year == 2023)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Kozhikode 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Kozhikode') & (data['Timestamp'].dt.year == 2023)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Kozhikode 2023', width=600, height=300)
     return chart


</code>
"
1018,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Bihar stations in 2019, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Bihar') & (data['Timestamp'].dt.year == 2019)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Bihar Stations 2019', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Bihar') & (data['Timestamp'].dt.year == 2019)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Bihar Stations 2019', width=450, height=350)


</code>
"
1019,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Haryana, Meghalaya, and Tripura across 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Haryana', 'Meghalaya', 'Tripura']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Haryana', 'Meghalaya', 'Tripura']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
     return chart


</code>
"
1020,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Nagaland, Puducherry, and Haryana across 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Nagaland', 'Puducherry', 'Haryana']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Nagaland', 'Puducherry', 'Haryana']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
     return chart


</code>
"
1021,temporal_aggregation,Plot the weekly average PM2.5 for Vijayapura in 2020 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Vijayapura') & (data['Timestamp'].dt.year == 2020)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Vijayapura 2020', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Vijayapura') & (data['Timestamp'].dt.year == 2020)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Vijayapura 2020', width=600, height=300)
     return chart


</code>
"
1022,temporal_aggregation,Show the monthly average PM10 trend for Hubballi from 2017 to 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Hubballi'].copy()
    df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Hubballi (2017–2022)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Hubballi'].copy()
     df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Hubballi (2017–2022)', width=600, height=300)
     return chart


</code>
"
1023,specific_pattern,Show a cumulative area chart of PM2.5 readings for Ballabgarh across 2023.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Ballabgarh') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Ballabgarh 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Ballabgarh') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Ballabgarh 2023', width=600, height=300)
     return chart


</code>
"
1024,temporal_aggregation,Show a monthly bar chart of the number of days West Bengal exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2021.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'West Bengal') & (data['Timestamp'].dt.year == 2021)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days West Bengal Exceeded WHO PM2.5 Guideline per Month – 2021', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'West Bengal') & (data['Timestamp'].dt.year == 2021)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days West Bengal Exceeded WHO PM2.5 Guideline per Month – 2021', width=500, height=300)
     return chart


</code>
"
1025,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Assam, Gujarat, and Haryana across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Assam', 'Gujarat', 'Haryana']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Assam', 'Gujarat', 'Haryana']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
1026,spatio_temporal_aggregation,Show a heatmap of average PM2.5 for the top 14 most polluted states by month for 2017.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    top8 = data.groupby('state')['PM2.5'].mean().nlargest(14).index.tolist()
    df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2017)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_rect().encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('state:N', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
        tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Heatmap – Top 14 Polluted States by Month (2017)', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     top8 = data.groupby('state')['PM2.5'].mean().nlargest(14).index.tolist()
     df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2017)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_rect().encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('state:N', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
         tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Heatmap – Top 14 Polluted States by Month (2017)', width=500, height=300)
     return chart


</code>
"
1027,temporal_aggregation,Show the monthly average PM2.5 for Thoothukudi in 2024 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Thoothukudi') & (data['Timestamp'].dt.year == 2024)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Thoothukudi 2024', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Thoothukudi') & (data['Timestamp'].dt.year == 2024)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Thoothukudi 2024', width=450, height=280)


</code>
"
1028,spatial_aggregation,Visualize the bottom 14 states with the lowest average PM2.5 in 2024 using a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2024]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nsmallest(14, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Bottom 14 States by Average PM2.5 in 2024', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2024]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nsmallest(14, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Bottom 14 States by Average PM2.5 in 2024', width=500, height=300)
     return chart


</code>
"
1029,specific_pattern,Plot the rolling 30-day average PM2.5 for Jharkhand in 2017 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Jharkhand') & (data['Timestamp'].dt.year == 2017)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Jharkhand 2017', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Jharkhand') & (data['Timestamp'].dt.year == 2017)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Jharkhand 2017', width=600, height=300)


</code>
"
1030,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Chhattisgarh, Andhra Pradesh, and Assam in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Chhattisgarh', 'Andhra Pradesh', 'Assam']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Chhattisgarh, Andhra Pradesh, UP – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Chhattisgarh', 'Andhra Pradesh', 'Assam']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Chhattisgarh, Andhra Pradesh, UP – 2023', width=550, height=320)
     return chart


</code>
"
1031,temporal_aggregation,Show the monthly average PM2.5 for Alwar in 2022 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Alwar') & (data['Timestamp'].dt.year == 2022)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Alwar 2022', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Alwar') & (data['Timestamp'].dt.year == 2022)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Alwar 2022', width=450, height=280)


</code>
"
1032,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Yadgir, Naharlagun, and Rajgir in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Yadgir', 'Naharlagun', 'Rajgir']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Yadgir vs Naharlagun vs Rajgir – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Yadgir', 'Naharlagun', 'Rajgir']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Yadgir vs Naharlagun vs Rajgir – 2023', width=550, height=320)
     return chart


</code>
"
1033,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Karnataka, Assam, and Puducherry in 2019 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Karnataka', 'Assam', 'Puducherry']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Karnataka, Assam, UP – 2019', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Karnataka', 'Assam', 'Puducherry']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Karnataka, Assam, UP – 2019', width=550, height=320)
     return chart


</code>
"
1034,temporal_aggregation,Plot the weekly average PM2.5 for Kalyan in 2021 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Kalyan') & (data['Timestamp'].dt.year == 2021)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Kalyan 2021', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Kalyan') & (data['Timestamp'].dt.year == 2021)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Kalyan 2021', width=600, height=300)
     return chart


</code>
"
1035,temporal_aggregation,Show the monthly average PM2.5 for Jaisalmer in 2018 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Jaisalmer') & (data['Timestamp'].dt.year == 2018)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Jaisalmer 2018', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Jaisalmer') & (data['Timestamp'].dt.year == 2018)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Jaisalmer 2018', width=450, height=280)


</code>
"
1036,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Jalore, Greater Noida, and Hubballi in 2018 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Jalore', 'Greater Noida', 'Hubballi']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2018)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Jalore vs Greater Noida vs Hubballi – 2018', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Jalore', 'Greater Noida', 'Hubballi']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2018)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Jalore vs Greater Noida vs Hubballi – 2018', width=550, height=320)
     return chart


</code>
"
1037,spatio_temporal_aggregation,Show a heatmap of average PM2.5 for the top 10 most polluted states by month for 2022.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    top8 = data.groupby('state')['PM2.5'].mean().nlargest(10).index.tolist()
    df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_rect().encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('state:N', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
        tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Heatmap – Top 10 Polluted States by Month (2022)', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     top8 = data.groupby('state')['PM2.5'].mean().nlargest(10).index.tolist()
     df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_rect().encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('state:N', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
         tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Heatmap – Top 10 Polluted States by Month (2022)', width=500, height=300)
     return chart


</code>
"
1038,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for West Bengal, Arunachal Pradesh, and Meghalaya across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['West Bengal', 'Arunachal Pradesh', 'Meghalaya']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['West Bengal', 'Arunachal Pradesh', 'Meghalaya']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
1039,specific_pattern,Plot the rolling 30-day average PM2.5 for Chhattisgarh in 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Chhattisgarh') & (data['Timestamp'].dt.year == 2024)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Chhattisgarh 2024', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Chhattisgarh') & (data['Timestamp'].dt.year == 2024)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Chhattisgarh 2024', width=600, height=300)


</code>
"
1040,specific_pattern,Show a cumulative area chart of PM2.5 readings for Rupnagar across 2022.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Rupnagar') & (data['Timestamp'].dt.year == 2022)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Rupnagar 2022', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Rupnagar') & (data['Timestamp'].dt.year == 2022)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Rupnagar 2022', width=600, height=300)
     return chart


</code>
"
1041,spatio_temporal_aggregation,Create a heatmap showing the average PM2.5 by month (x-axis) and year (y-axis) for Punjab.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Punjab'].copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_rect().encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Year:N', title='Year'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='Avg PM2.5'),
        tooltip=['Year:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Heatmap – Punjab (Month × Year)', width=500, height=280)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Punjab'].copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_rect().encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Year:N', title='Year'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='Avg PM2.5'),
         tooltip=['Year:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Heatmap – Punjab (Month × Year)', width=500, height=280)
     return chart


</code>
"
1042,spatio_temporal_aggregation,Show a heatmap of average PM2.5 for the top 12 most polluted states by month for 2018.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    top8 = data.groupby('state')['PM2.5'].mean().nlargest(12).index.tolist()
    df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2018)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_rect().encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('state:N', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
        tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Heatmap – Top 12 Polluted States by Month (2018)', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     top8 = data.groupby('state')['PM2.5'].mean().nlargest(12).index.tolist()
     df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2018)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_rect().encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('state:N', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
         tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Heatmap – Top 12 Polluted States by Month (2018)', width=500, height=300)
     return chart


</code>
"
1043,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Kerala, Sikkim, and Punjab across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Kerala', 'Sikkim', 'Punjab']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Kerala', 'Sikkim', 'Punjab']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
1044,spatio_temporal_aggregation,Show a heatmap of average PM2.5 for the top 11 most polluted states by month for 2019.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    top8 = data.groupby('state')['PM2.5'].mean().nlargest(11).index.tolist()
    df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_rect().encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('state:N', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
        tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Heatmap – Top 11 Polluted States by Month (2019)', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     top8 = data.groupby('state')['PM2.5'].mean().nlargest(11).index.tolist()
     df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_rect().encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('state:N', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
         tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Heatmap – Top 11 Polluted States by Month (2019)', width=500, height=300)
     return chart


</code>
"
1045,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Telangana, Rajasthan, and Bihar from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Telangana', 'Rajasthan', 'Bihar']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Telangana vs Rajasthan vs Bihar', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Telangana', 'Rajasthan', 'Bihar']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Telangana vs Rajasthan vs Bihar', width=550, height=320)
     return chart


</code>
"
1046,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Chandigarh stations in 2020, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Chandigarh') & (data['Timestamp'].dt.year == 2020)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Chandigarh Stations 2020', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Chandigarh') & (data['Timestamp'].dt.year == 2020)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Chandigarh Stations 2020', width=450, height=350)


</code>
"
1047,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Bihar Sharif, Pali, and Bahadurgarh in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Bihar Sharif', 'Pali', 'Bahadurgarh']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Bihar Sharif vs Pali vs Bahadurgarh – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Bihar Sharif', 'Pali', 'Bahadurgarh']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Bihar Sharif vs Pali vs Bahadurgarh – 2022', width=550, height=320)
     return chart


</code>
"
1048,temporal_aggregation,Plot the weekly average PM2.5 for Jhunjhunu in 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Jhunjhunu') & (data['Timestamp'].dt.year == 2024)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Jhunjhunu 2024', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Jhunjhunu') & (data['Timestamp'].dt.year == 2024)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Jhunjhunu 2024', width=600, height=300)
     return chart


</code>
"
1049,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Virudhunagar, Visakhapatnam, and Virudhunagar in 2019 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Virudhunagar', 'Visakhapatnam', 'Virudhunagar']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Virudhunagar vs Visakhapatnam vs Virudhunagar – 2019', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Virudhunagar', 'Visakhapatnam', 'Virudhunagar']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Virudhunagar vs Visakhapatnam vs Virudhunagar – 2019', width=550, height=320)
     return chart


</code>
"
1050,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Chandigarh, Pathardih, and Amravati in 2019 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Chandigarh', 'Pathardih', 'Amravati']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Chandigarh vs Pathardih vs Amravati – 2019', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Chandigarh', 'Pathardih', 'Amravati']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Chandigarh vs Pathardih vs Amravati – 2019', width=550, height=320)
     return chart


</code>
"
1051,specific_pattern,Plot the rolling 30-day average PM2.5 for Delhi in 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Delhi') & (data['Timestamp'].dt.year == 2022)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Delhi 2022', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Delhi') & (data['Timestamp'].dt.year == 2022)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Delhi 2022', width=600, height=300)


</code>
"
1052,spatio_temporal_aggregation,Create a heatmap showing the average PM2.5 by month (x-axis) and year (y-axis) for Jammu and Kashmir.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Jammu and Kashmir'].copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_rect().encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Year:N', title='Year'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='Avg PM2.5'),
        tooltip=['Year:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Heatmap – Jammu and Kashmir (Month × Year)', width=500, height=280)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Jammu and Kashmir'].copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_rect().encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Year:N', title='Year'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='Avg PM2.5'),
         tooltip=['Year:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Heatmap – Jammu and Kashmir (Month × Year)', width=500, height=280)
     return chart


</code>
"
1053,specific_pattern,Show PM2.5 exceedances above the Indian standard (60 µg/m³) per year as a bar chart for Haryana.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Haryana'].dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 60].copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby('Year')['Timestamp'].nunique().reset_index()
    df.columns = ['Year','Days Exceeded']
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('Days Exceeded:Q', title='Days PM2.5 > 60 µg/m³'),
        tooltip=['Year:N','Days Exceeded:Q']
    ).properties(title='Days Haryana Exceeded Indian PM2.5 Standard (60 µg/m³) per Year', width=450, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Haryana'].dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 60].copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby('Year')['Timestamp'].nunique().reset_index()
     df.columns = ['Year','Days Exceeded']
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('Days Exceeded:Q', title='Days PM2.5 > 60 µg/m³'),
         tooltip=['Year:N','Days Exceeded:Q']
     ).properties(title='Days Haryana Exceeded Indian PM2.5 Standard (60 µg/m³) per Year', width=450, height=300)
     return chart


</code>
"
1054,specific_pattern,Show a cumulative area chart of PM2.5 readings for Bhagalpur across 2023.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Bhagalpur') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Bhagalpur 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Bhagalpur') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Bhagalpur 2023', width=600, height=300)
     return chart


</code>
"
1055,temporal_aggregation,Plot the weekly average PM2.5 for Pali in 2018 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Pali') & (data['Timestamp'].dt.year == 2018)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Pali 2018', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Pali') & (data['Timestamp'].dt.year == 2018)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Pali 2018', width=600, height=300)
     return chart


</code>
"
1056,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Navi Mumbai, Bhilwara, and Dausa in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Navi Mumbai', 'Bhilwara', 'Dausa']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Navi Mumbai vs Bhilwara vs Dausa – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Navi Mumbai', 'Bhilwara', 'Dausa']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Navi Mumbai vs Bhilwara vs Dausa – 2022', width=550, height=320)
     return chart


</code>
"
1057,temporal_aggregation,Show the monthly average PM10 trend for Ankleshwar from 2017 to 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Ankleshwar'].copy()
    df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Ankleshwar (2017–2022)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Ankleshwar'].copy()
     df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Ankleshwar (2017–2022)', width=600, height=300)
     return chart


</code>
"
1058,spatio_temporal_aggregation,Show a heatmap of average PM2.5 for the top 9 most polluted states by month for 2021.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    top8 = data.groupby('state')['PM2.5'].mean().nlargest(9).index.tolist()
    df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2021)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_rect().encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('state:N', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
        tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Heatmap – Top 9 Polluted States by Month (2021)', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     top8 = data.groupby('state')['PM2.5'].mean().nlargest(9).index.tolist()
     df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2021)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_rect().encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('state:N', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
         tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Heatmap – Top 9 Polluted States by Month (2021)', width=500, height=300)
     return chart


</code>
"
1059,temporal_aggregation,Show the monthly average PM10 trend for Katihar from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Katihar'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Katihar (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Katihar'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Katihar (2019–2024)', width=600, height=300)
     return chart


</code>
"
1060,spatial_aggregation,Plot the top 12 states by average PM2.5 in 2023 as a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2023]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nlargest(12, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 12 States by Average PM2.5 in 2023', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2023]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nlargest(12, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 12 States by Average PM2.5 in 2023', width=500, height=300)
     return chart


</code>
"
1061,spatial_aggregation,Plot the top 11 states by average PM2.5 in 2024 as a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2024]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nlargest(11, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 11 States by Average PM2.5 in 2024', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2024]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nlargest(11, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 11 States by Average PM2.5 in 2024', width=500, height=300)
     return chart


</code>
"
1062,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Tamil Nadu, Meghalaya, and Andhra Pradesh from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Tamil Nadu', 'Meghalaya', 'Andhra Pradesh']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Tamil Nadu vs Meghalaya vs Andhra Pradesh', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Tamil Nadu', 'Meghalaya', 'Andhra Pradesh']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Tamil Nadu vs Meghalaya vs Andhra Pradesh', width=550, height=320)
     return chart


</code>
"
1063,temporal_aggregation,Show a monthly bar chart of the number of days Rajasthan exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2023.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Rajasthan') & (data['Timestamp'].dt.year == 2023)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Rajasthan Exceeded WHO PM2.5 Guideline per Month – 2023', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Rajasthan') & (data['Timestamp'].dt.year == 2023)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Rajasthan Exceeded WHO PM2.5 Guideline per Month – 2023', width=500, height=300)
     return chart


</code>
"
1064,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Thiruvananthapuram, Jaipur, and Pratapgarh in 2017 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Thiruvananthapuram', 'Jaipur', 'Pratapgarh']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2017)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Thiruvananthapuram vs Jaipur vs Pratapgarh – 2017', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Thiruvananthapuram', 'Jaipur', 'Pratapgarh']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2017)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Thiruvananthapuram vs Jaipur vs Pratapgarh – 2017', width=550, height=320)
     return chart


</code>
"
1065,specific_pattern,Show a cumulative area chart of PM2.5 readings for Kannur across 2023.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Kannur') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Kannur 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Kannur') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Kannur 2023', width=600, height=300)
     return chart


</code>
"
1066,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Tripura stations in 2018, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Tripura') & (data['Timestamp'].dt.year == 2018)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Tripura Stations 2018', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Tripura') & (data['Timestamp'].dt.year == 2018)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Tripura Stations 2018', width=450, height=350)


</code>
"
1067,spatial_aggregation,Plot the distribution of PM2.5 values in Madhya Pradesh across all years using a histogram.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Madhya Pradesh'].dropna(subset=['PM2.5'])
    df = df[['PM2.5']]
    chart = alt.Chart(df).mark_bar(color='steelblue', opacity=0.75).encode(
        x=alt.X('PM2\.5:Q', bin=alt.Bin(maxbins=40), title='PM2.5 (µg/m³)'),
        y=alt.Y('count()', title='Number of Observations'),
        tooltip=[alt.Tooltip('PM2\.5:Q', bin=True), 'count()']
    ).properties(title='PM2.5 Distribution – Madhya Pradesh (All Years)', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Madhya Pradesh'].dropna(subset=['PM2.5'])
     df = df[['PM2.5']]
     chart = alt.Chart(df).mark_bar(color='steelblue', opacity=0.75).encode(
         x=alt.X('PM2\.5:Q', bin=alt.Bin(maxbins=40), title='PM2.5 (µg/m³)'),
         y=alt.Y('count()', title='Number of Observations'),
         tooltip=[alt.Tooltip('PM2\.5:Q', bin=True), 'count()']
     ).properties(title='PM2.5 Distribution – Madhya Pradesh (All Years)', width=500, height=300)
     return chart


</code>
"
1068,spatial_aggregation,"Show the top 6 states by average PM10 in 2018 as a bar chart, sorted in descending order.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2018]
    df = df.groupby('state')['PM10'].mean().reset_index().dropna()
    df = df.nlargest(6, 'PM10')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Top 6 States by Average PM10 in 2018', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2018]
     df = df.groupby('state')['PM10'].mean().reset_index().dropna()
     df = df.nlargest(6, 'PM10')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Top 6 States by Average PM10 in 2018', width=500, height=300)
     return chart


</code>
"
1069,temporal_aggregation,Show the monthly average PM10 trend for Aizawl from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Aizawl'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Aizawl (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Aizawl'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Aizawl (2019–2024)', width=600, height=300)
     return chart


</code>
"
1070,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Assam, Andhra Pradesh, and Chandigarh from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Assam', 'Andhra Pradesh', 'Chandigarh']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Assam vs Andhra Pradesh vs Chandigarh', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Assam', 'Andhra Pradesh', 'Chandigarh']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Assam vs Andhra Pradesh vs Chandigarh', width=550, height=320)
     return chart


</code>
"
1071,temporal_aggregation,Show the monthly average PM2.5 for Jhalawar in 2024 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Jhalawar') & (data['Timestamp'].dt.year == 2024)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Jhalawar 2024', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Jhalawar') & (data['Timestamp'].dt.year == 2024)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Jhalawar 2024', width=450, height=280)


</code>
"
1072,spatial_aggregation,Plot the top 8 states by average PM2.5 in 2017 as a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2017]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nlargest(8, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 8 States by Average PM2.5 in 2017', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2017]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nlargest(8, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 8 States by Average PM2.5 in 2017', width=500, height=300)
     return chart


</code>
"
1073,spatial_aggregation,Visualize the bottom 13 states with the lowest average PM2.5 in 2023 using a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2023]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nsmallest(13, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Bottom 13 States by Average PM2.5 in 2023', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2023]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nsmallest(13, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Bottom 13 States by Average PM2.5 in 2023', width=500, height=300)
     return chart


</code>
"
1074,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Mumbai, Ghaziabad, and Kochi in 2020 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Mumbai', 'Ghaziabad', 'Kochi']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2020)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Mumbai vs Ghaziabad vs Kochi – 2020', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Mumbai', 'Ghaziabad', 'Kochi']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2020)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Mumbai vs Ghaziabad vs Kochi – 2020', width=550, height=320)
     return chart


</code>
"
1075,temporal_aggregation,Plot the monthly average PM2.5 trend for Gujarat from 2017 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Gujarat'].copy()
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='steelblue').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly Average PM2.5 Trend – Gujarat (2017–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Gujarat'].copy()
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='steelblue').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly Average PM2.5 Trend – Gujarat (2017–2024)', width=600, height=300)
     return chart


</code>
"
1076,temporal_aggregation,Show a monthly bar chart of the number of days Haryana exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2024.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Haryana') & (data['Timestamp'].dt.year == 2024)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Haryana Exceeded WHO PM2.5 Guideline per Month – 2024', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Haryana') & (data['Timestamp'].dt.year == 2024)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Haryana Exceeded WHO PM2.5 Guideline per Month – 2024', width=500, height=300)
     return chart


</code>
"
1077,spatio_temporal_aggregation,Show a heatmap of average PM2.5 for the top 6 most polluted states by month for 2024.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    top8 = data.groupby('state')['PM2.5'].mean().nlargest(6).index.tolist()
    df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_rect().encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('state:N', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
        tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Heatmap – Top 6 Polluted States by Month (2024)', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     top8 = data.groupby('state')['PM2.5'].mean().nlargest(6).index.tolist()
     df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_rect().encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('state:N', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
         tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Heatmap – Top 6 Polluted States by Month (2024)', width=500, height=300)
     return chart


</code>
"
1078,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Assam, Maharashtra, and Puducherry in 2018 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Assam', 'Maharashtra', 'Puducherry']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2018)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Assam, Maharashtra, UP – 2018', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Assam', 'Maharashtra', 'Puducherry']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2018)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Assam, Maharashtra, UP – 2018', width=550, height=320)
     return chart


</code>
"
1079,spatial_aggregation,Plot the distribution of PM2.5 values in Arunachal Pradesh across all years using a histogram.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Arunachal Pradesh'].dropna(subset=['PM2.5'])
    df = df[['PM2.5']]
    chart = alt.Chart(df).mark_bar(color='steelblue', opacity=0.75).encode(
        x=alt.X('PM2\.5:Q', bin=alt.Bin(maxbins=40), title='PM2.5 (µg/m³)'),
        y=alt.Y('count()', title='Number of Observations'),
        tooltip=[alt.Tooltip('PM2\.5:Q', bin=True), 'count()']
    ).properties(title='PM2.5 Distribution – Arunachal Pradesh (All Years)', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Arunachal Pradesh'].dropna(subset=['PM2.5'])
     df = df[['PM2.5']]
     chart = alt.Chart(df).mark_bar(color='steelblue', opacity=0.75).encode(
         x=alt.X('PM2\.5:Q', bin=alt.Bin(maxbins=40), title='PM2.5 (µg/m³)'),
         y=alt.Y('count()', title='Number of Observations'),
         tooltip=[alt.Tooltip('PM2\.5:Q', bin=True), 'count()']
     ).properties(title='PM2.5 Distribution – Arunachal Pradesh (All Years)', width=500, height=300)
     return chart


</code>
"
1080,temporal_aggregation,Show the monthly average PM10 trend for Imphal from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Imphal'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Imphal (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Imphal'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Imphal (2019–2024)', width=600, height=300)
     return chart


</code>
"
1081,spatio_temporal_aggregation,Create a heatmap showing the average PM2.5 by month (x-axis) and year (y-axis) for Meghalaya.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Meghalaya'].copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_rect().encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Year:N', title='Year'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='Avg PM2.5'),
        tooltip=['Year:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Heatmap – Meghalaya (Month × Year)', width=500, height=280)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Meghalaya'].copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_rect().encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Year:N', title='Year'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='Avg PM2.5'),
         tooltip=['Year:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Heatmap – Meghalaya (Month × Year)', width=500, height=280)
     return chart


</code>
"
1082,spatial_aggregation,"Show the top 11 states by average PM10 in 2023 as a bar chart, sorted in descending order.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2023]
    df = df.groupby('state')['PM10'].mean().reset_index().dropna()
    df = df.nlargest(11, 'PM10')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Top 11 States by Average PM10 in 2023', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2023]
     df = df.groupby('state')['PM10'].mean().reset_index().dropna()
     df = df.nlargest(11, 'PM10')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Top 11 States by Average PM10 in 2023', width=500, height=300)
     return chart


</code>
"
1083,temporal_aggregation,Show the monthly average PM10 trend for Baddi from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Baddi'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Baddi (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Baddi'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Baddi (2019–2024)', width=600, height=300)
     return chart


</code>
"
1084,temporal_aggregation,Show the monthly average PM2.5 for Fatehabad in 2022 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Fatehabad') & (data['Timestamp'].dt.year == 2022)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Fatehabad 2022', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Fatehabad') & (data['Timestamp'].dt.year == 2022)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Fatehabad 2022', width=450, height=280)


</code>
"
1085,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Agartala, Rishikesh, and Muzaffarpur in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Agartala', 'Rishikesh', 'Muzaffarpur']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Agartala vs Rishikesh vs Muzaffarpur – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Agartala', 'Rishikesh', 'Muzaffarpur']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Agartala vs Rishikesh vs Muzaffarpur – 2022', width=550, height=320)
     return chart


</code>
"
1086,spatial_aggregation,"Show the top 10 states by average PM10 in 2022 as a bar chart, sorted in descending order.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2022]
    df = df.groupby('state')['PM10'].mean().reset_index().dropna()
    df = df.nlargest(10, 'PM10')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Top 10 States by Average PM10 in 2022', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2022]
     df = df.groupby('state')['PM10'].mean().reset_index().dropna()
     df = df.nlargest(10, 'PM10')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Top 10 States by Average PM10 in 2022', width=500, height=300)
     return chart


</code>
"
1087,spatial_aggregation,Show a bar chart of the top 13 cities by median PM2.5 in 2023.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2023]
    df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
    df = df.nlargest(13, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
        y=alt.Y('city:N', sort='-x', title='City'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
        tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 13 Cities by Median PM2.5 in 2023', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2023]
     df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
     df = df.nlargest(13, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
         y=alt.Y('city:N', sort='-x', title='City'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
         tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 13 Cities by Median PM2.5 in 2023', width=500, height=300)
     return chart


</code>
"
1088,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Nagaland, Assam, and Punjab from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Nagaland', 'Assam', 'Punjab']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Nagaland vs Assam vs Punjab', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Nagaland', 'Assam', 'Punjab']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Nagaland vs Assam vs Punjab', width=550, height=320)
     return chart


</code>
"
1089,spatial_aggregation,Plot the top 15 states by average PM2.5 in 2018 as a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2018]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nlargest(15, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 15 States by Average PM2.5 in 2018', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2018]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nlargest(15, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 15 States by Average PM2.5 in 2018', width=500, height=300)
     return chart


</code>
"
1090,spatio_temporal_aggregation,Show a heatmap of average PM2.5 for the top 11 most polluted states by month for 2022.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    top8 = data.groupby('state')['PM2.5'].mean().nlargest(11).index.tolist()
    df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_rect().encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('state:N', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
        tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Heatmap – Top 11 Polluted States by Month (2022)', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     top8 = data.groupby('state')['PM2.5'].mean().nlargest(11).index.tolist()
     df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_rect().encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('state:N', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
         tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Heatmap – Top 11 Polluted States by Month (2022)', width=500, height=300)
     return chart


</code>
"
1091,spatial_aggregation,Show a bar chart of the top 9 cities by median PM2.5 in 2024.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2024]
    df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
    df = df.nlargest(9, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
        y=alt.Y('city:N', sort='-x', title='City'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
        tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 9 Cities by Median PM2.5 in 2024', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2024]
     df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
     df = df.nlargest(9, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
         y=alt.Y('city:N', sort='-x', title='City'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
         tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 9 Cities by Median PM2.5 in 2024', width=500, height=300)
     return chart


</code>
"
1092,spatial_aggregation,Show a bar chart of the top 13 cities by median PM2.5 in 2018.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2018]
    df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
    df = df.nlargest(13, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
        y=alt.Y('city:N', sort='-x', title='City'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
        tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 13 Cities by Median PM2.5 in 2018', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2018]
     df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
     df = df.nlargest(13, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
         y=alt.Y('city:N', sort='-x', title='City'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
         tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 13 Cities by Median PM2.5 in 2018', width=500, height=300)
     return chart


</code>
"
1093,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Meghalaya, Delhi, and Karnataka from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Meghalaya', 'Delhi', 'Karnataka']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Meghalaya vs Delhi vs Karnataka', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Meghalaya', 'Delhi', 'Karnataka']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Meghalaya vs Delhi vs Karnataka', width=550, height=320)
     return chart


</code>
"
1094,temporal_aggregation,Show the monthly average PM2.5 for Jalore in 2022 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Jalore') & (data['Timestamp'].dt.year == 2022)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Jalore 2022', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Jalore') & (data['Timestamp'].dt.year == 2022)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Jalore 2022', width=450, height=280)


</code>
"
1095,specific_pattern,Show PM2.5 exceedances above the Indian standard (60 µg/m³) per year as a bar chart for Chhattisgarh.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Chhattisgarh'].dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 60].copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby('Year')['Timestamp'].nunique().reset_index()
    df.columns = ['Year','Days Exceeded']
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('Days Exceeded:Q', title='Days PM2.5 > 60 µg/m³'),
        tooltip=['Year:N','Days Exceeded:Q']
    ).properties(title='Days Chhattisgarh Exceeded Indian PM2.5 Standard (60 µg/m³) per Year', width=450, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Chhattisgarh'].dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 60].copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby('Year')['Timestamp'].nunique().reset_index()
     df.columns = ['Year','Days Exceeded']
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('Days Exceeded:Q', title='Days PM2.5 > 60 µg/m³'),
         tooltip=['Year:N','Days Exceeded:Q']
     ).properties(title='Days Chhattisgarh Exceeded Indian PM2.5 Standard (60 µg/m³) per Year', width=450, height=300)
     return chart


</code>
"
1096,spatio_temporal_aggregation,Show a heatmap of average PM2.5 for the top 15 most polluted states by month for 2021.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    top8 = data.groupby('state')['PM2.5'].mean().nlargest(15).index.tolist()
    df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2021)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_rect().encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('state:N', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
        tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Heatmap – Top 15 Polluted States by Month (2021)', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     top8 = data.groupby('state')['PM2.5'].mean().nlargest(15).index.tolist()
     df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2021)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_rect().encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('state:N', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
         tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Heatmap – Top 15 Polluted States by Month (2021)', width=500, height=300)
     return chart


</code>
"
1097,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Nagaland, Mizoram, and Gujarat across 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Nagaland', 'Mizoram', 'Gujarat']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Nagaland', 'Mizoram', 'Gujarat']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
     return chart


</code>
"
1098,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Pali, Ramanathapuram, and Ratlam in 2019 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Pali', 'Ramanathapuram', 'Ratlam']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Pali vs Ramanathapuram vs Ratlam – 2019', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Pali', 'Ramanathapuram', 'Ratlam']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Pali vs Ramanathapuram vs Ratlam – 2019', width=550, height=320)
     return chart


</code>
"
1099,spatial_aggregation,Plot the top 5 states by average PM2.5 in 2023 as a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2023]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nlargest(5, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 5 States by Average PM2.5 in 2023', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2023]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nlargest(5, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 5 States by Average PM2.5 in 2023', width=500, height=300)
     return chart


</code>
"
1100,spatial_aggregation,"Show the top 7 states by average PM10 in 2022 as a bar chart, sorted in descending order.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2022]
    df = df.groupby('state')['PM10'].mean().reset_index().dropna()
    df = df.nlargest(7, 'PM10')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Top 7 States by Average PM10 in 2022', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2022]
     df = df.groupby('state')['PM10'].mean().reset_index().dropna()
     df = df.nlargest(7, 'PM10')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Top 7 States by Average PM10 in 2022', width=500, height=300)
     return chart


</code>
"
1101,spatio_temporal_aggregation,"Create a faceted bar chart showing top 9 states by average PM2.5 per year for 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year.isin([2017,2018,2019,2020])].copy()
    df['Year'] = df['Timestamp'].dt.year
    agg = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    top = agg.groupby('Year').apply(lambda x: x.nlargest(9,'PM2.5')).reset_index(drop=True)
    chart = alt.Chart(top).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Avg PM2.5'),
        y=alt.Y('state:N', sort='-x'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).facet(facet='Year:O', columns=2).properties(title='Top 9 States by PM2.5 per Year')
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year.isin([2017,2018,2019,2020])].copy()
     df['Year'] = df['Timestamp'].dt.year
     agg = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     top = agg.groupby('Year').apply(lambda x: x.nlargest(9,'PM2.5')).reset_index(drop=True)
     chart = alt.Chart(top).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Avg PM2.5'),
         y=alt.Y('state:N', sort='-x'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).facet(facet='Year:O', columns=2).properties(title='Top 9 States by PM2.5 per Year')
     return chart


</code>
"
1102,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Delhi, Jammu and Kashmir, and Tripura from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Delhi', 'Jammu and Kashmir', 'Tripura']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Delhi vs Jammu and Kashmir vs Tripura', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Delhi', 'Jammu and Kashmir', 'Tripura']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Delhi vs Jammu and Kashmir vs Tripura', width=550, height=320)
     return chart


</code>
"
1103,spatial_aggregation,"Show the top 9 states by average PM10 in 2024 as a bar chart, sorted in descending order.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2024]
    df = df.groupby('state')['PM10'].mean().reset_index().dropna()
    df = df.nlargest(9, 'PM10')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Top 9 States by Average PM10 in 2024', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2024]
     df = df.groupby('state')['PM10'].mean().reset_index().dropna()
     df = df.nlargest(9, 'PM10')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Top 9 States by Average PM10 in 2024', width=500, height=300)
     return chart


</code>
"
1104,temporal_aggregation,Show the monthly average PM10 trend for Muzaffarpur from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Muzaffarpur'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Muzaffarpur (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Muzaffarpur'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Muzaffarpur (2019–2024)', width=600, height=300)
     return chart


</code>
"
1105,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Haryana, Himachal Pradesh, and Tamil Nadu from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Haryana', 'Himachal Pradesh', 'Tamil Nadu']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Haryana vs Himachal Pradesh vs Tamil Nadu', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Haryana', 'Himachal Pradesh', 'Tamil Nadu']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Haryana vs Himachal Pradesh vs Tamil Nadu', width=550, height=320)
     return chart


</code>
"
1106,temporal_aggregation,Plot the weekly average PM2.5 for Bettiah in 2023 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Bettiah') & (data['Timestamp'].dt.year == 2023)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Bettiah 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Bettiah') & (data['Timestamp'].dt.year == 2023)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Bettiah 2023', width=600, height=300)
     return chart


</code>
"
1107,spatio_temporal_aggregation,Create a heatmap showing the average PM2.5 by month (x-axis) and year (y-axis) for Tamil Nadu.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Tamil Nadu'].copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_rect().encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Year:N', title='Year'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='Avg PM2.5'),
        tooltip=['Year:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Heatmap – Tamil Nadu (Month × Year)', width=500, height=280)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Tamil Nadu'].copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_rect().encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Year:N', title='Year'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='Avg PM2.5'),
         tooltip=['Year:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Heatmap – Tamil Nadu (Month × Year)', width=500, height=280)
     return chart


</code>
"
1108,specific_pattern,Show a cumulative area chart of PM2.5 readings for Bhubaneswar across 2023.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Bhubaneswar') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Bhubaneswar 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Bhubaneswar') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Bhubaneswar 2023', width=600, height=300)
     return chart


</code>
"
1109,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Uttarakhand, Bihar, and Tripura across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Uttarakhand', 'Bihar', 'Tripura']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Uttarakhand', 'Bihar', 'Tripura']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
1110,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Rajasthan, Tripura, and Odisha from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Rajasthan', 'Tripura', 'Odisha']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Rajasthan vs Tripura vs Odisha', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Rajasthan', 'Tripura', 'Odisha']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Rajasthan vs Tripura vs Odisha', width=550, height=320)
     return chart


</code>
"
1111,spatio_temporal_aggregation,Create a heatmap showing the average PM2.5 by month (x-axis) and year (y-axis) for Manipur.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Manipur'].copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_rect().encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Year:N', title='Year'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='Avg PM2.5'),
        tooltip=['Year:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Heatmap – Manipur (Month × Year)', width=500, height=280)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Manipur'].copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_rect().encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Year:N', title='Year'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='Avg PM2.5'),
         tooltip=['Year:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Heatmap – Manipur (Month × Year)', width=500, height=280)
     return chart


</code>
"
1112,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Himachal Pradesh, Tamil Nadu, and Himachal Pradesh from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Himachal Pradesh', 'Tamil Nadu', 'Himachal Pradesh']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Himachal Pradesh vs Tamil Nadu vs Himachal Pradesh', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Himachal Pradesh', 'Tamil Nadu', 'Himachal Pradesh']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Himachal Pradesh vs Tamil Nadu vs Himachal Pradesh', width=550, height=320)
     return chart


</code>
"
1113,temporal_aggregation,Show the monthly average PM10 trend for Bhilai from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Bhilai'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Bhilai (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Bhilai'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Bhilai (2019–2024)', width=600, height=300)
     return chart


</code>
"
1114,specific_pattern,Show a cumulative area chart of PM2.5 readings for Chikkaballapur across 2018.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Chikkaballapur') & (data['Timestamp'].dt.year == 2018)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Chikkaballapur 2018', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Chikkaballapur') & (data['Timestamp'].dt.year == 2018)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Chikkaballapur 2018', width=600, height=300)
     return chart


</code>
"
1115,temporal_aggregation,Show the monthly average PM2.5 for Ooty in 2020 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Ooty') & (data['Timestamp'].dt.year == 2020)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Ooty 2020', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Ooty') & (data['Timestamp'].dt.year == 2020)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Ooty 2020', width=450, height=280)


</code>
"
1116,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Sikkim, Haryana, and Kerala in 2017 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Sikkim', 'Haryana', 'Kerala']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2017)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Sikkim, Haryana, UP – 2017', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Sikkim', 'Haryana', 'Kerala']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2017)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Sikkim, Haryana, UP – 2017', width=550, height=320)
     return chart


</code>
"
1117,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Jharkhand stations in 2020, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Jharkhand') & (data['Timestamp'].dt.year == 2020)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Jharkhand Stations 2020', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Jharkhand') & (data['Timestamp'].dt.year == 2020)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Jharkhand Stations 2020', width=450, height=350)


</code>
"
1118,temporal_aggregation,Show the monthly average PM2.5 for Manesar in 2019 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Manesar') & (data['Timestamp'].dt.year == 2019)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Manesar 2019', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Manesar') & (data['Timestamp'].dt.year == 2019)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Manesar 2019', width=450, height=280)


</code>
"
1119,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Delhi, Jharkhand, and Puducherry from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Delhi', 'Jharkhand', 'Puducherry']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Delhi vs Jharkhand vs Puducherry', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Delhi', 'Jharkhand', 'Puducherry']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Delhi vs Jharkhand vs Puducherry', width=550, height=320)
     return chart


</code>
"
1120,spatial_aggregation,"Show the top 8 states by average PM10 in 2020 as a bar chart, sorted in descending order.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2020]
    df = df.groupby('state')['PM10'].mean().reset_index().dropna()
    df = df.nlargest(8, 'PM10')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Top 8 States by Average PM10 in 2020', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2020]
     df = df.groupby('state')['PM10'].mean().reset_index().dropna()
     df = df.nlargest(8, 'PM10')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Top 8 States by Average PM10 in 2020', width=500, height=300)
     return chart


</code>
"
1121,specific_pattern,Show a cumulative area chart of PM2.5 readings for Ambala across 2023.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Ambala') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Ambala 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Ambala') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Ambala 2023', width=600, height=300)
     return chart


</code>
"
1122,spatial_aggregation,Visualize the bottom 7 states with the lowest average PM2.5 in 2019 using a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2019]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nsmallest(7, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Bottom 7 States by Average PM2.5 in 2019', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2019]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nsmallest(7, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Bottom 7 States by Average PM2.5 in 2019', width=500, height=300)
     return chart


</code>
"
1123,temporal_aggregation,Show the monthly average PM2.5 for Kanchipuram in 2024 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Kanchipuram') & (data['Timestamp'].dt.year == 2024)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Kanchipuram 2024', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Kanchipuram') & (data['Timestamp'].dt.year == 2024)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Kanchipuram 2024', width=450, height=280)


</code>
"
1124,temporal_aggregation,Show the monthly average PM2.5 for Rourkela in 2022 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Rourkela') & (data['Timestamp'].dt.year == 2022)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Rourkela 2022', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Rourkela') & (data['Timestamp'].dt.year == 2022)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Rourkela 2022', width=450, height=280)


</code>
"
1125,specific_pattern,Plot the rolling 30-day average PM2.5 for Jharkhand in 2019 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Jharkhand') & (data['Timestamp'].dt.year == 2019)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Jharkhand 2019', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Jharkhand') & (data['Timestamp'].dt.year == 2019)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Jharkhand 2019', width=600, height=300)


</code>
"
1126,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Tamil Nadu, Haryana, and Arunachal Pradesh in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Tamil Nadu', 'Haryana', 'Arunachal Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Tamil Nadu, Haryana, UP – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Tamil Nadu', 'Haryana', 'Arunachal Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Tamil Nadu, Haryana, UP – 2022', width=550, height=320)
     return chart


</code>
"
1127,spatial_aggregation,"Show the top 9 states by average PM10 in 2022 as a bar chart, sorted in descending order.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2022]
    df = df.groupby('state')['PM10'].mean().reset_index().dropna()
    df = df.nlargest(9, 'PM10')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Top 9 States by Average PM10 in 2022', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2022]
     df = df.groupby('state')['PM10'].mean().reset_index().dropna()
     df = df.nlargest(9, 'PM10')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Top 9 States by Average PM10 in 2022', width=500, height=300)
     return chart


</code>
"
1128,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Chandigarh, Uttar Pradesh, and Jammu and Kashmir from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Chandigarh', 'Uttar Pradesh', 'Jammu and Kashmir']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Chandigarh vs Uttar Pradesh vs Jammu and Kashmir', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Chandigarh', 'Uttar Pradesh', 'Jammu and Kashmir']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Chandigarh vs Uttar Pradesh vs Jammu and Kashmir', width=550, height=320)
     return chart


</code>
"
1129,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Gujarat, Mizoram, and Gujarat from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Gujarat', 'Mizoram', 'Gujarat']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Gujarat vs Mizoram vs Gujarat', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Gujarat', 'Mizoram', 'Gujarat']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Gujarat vs Mizoram vs Gujarat', width=550, height=320)
     return chart


</code>
"
1130,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Uttarakhand, Jammu and Kashmir, and Nagaland across 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Uttarakhand', 'Jammu and Kashmir', 'Nagaland']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Uttarakhand', 'Jammu and Kashmir', 'Nagaland']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
     return chart


</code>
"
1131,temporal_aggregation,Plot the weekly average PM2.5 for Chikkamagaluru in 2023 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Chikkamagaluru') & (data['Timestamp'].dt.year == 2023)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Chikkamagaluru 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Chikkamagaluru') & (data['Timestamp'].dt.year == 2023)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Chikkamagaluru 2023', width=600, height=300)
     return chart


</code>
"
1132,temporal_aggregation,Show the monthly average PM10 trend for Tirunelveli from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Tirunelveli'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Tirunelveli (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Tirunelveli'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Tirunelveli (2019–2024)', width=600, height=300)
     return chart


</code>
"
1133,temporal_aggregation,Show the monthly average PM10 trend for Coimbatore from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Coimbatore'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Coimbatore (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Coimbatore'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Coimbatore (2019–2024)', width=600, height=300)
     return chart


</code>
"
1134,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Himachal Pradesh stations in 2020, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Himachal Pradesh') & (data['Timestamp'].dt.year == 2020)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Himachal Pradesh Stations 2020', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Himachal Pradesh') & (data['Timestamp'].dt.year == 2020)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Himachal Pradesh Stations 2020', width=450, height=350)


</code>
"
1135,temporal_aggregation,Show the monthly average PM2.5 for Chhal in 2017 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Chhal') & (data['Timestamp'].dt.year == 2017)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Chhal 2017', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Chhal') & (data['Timestamp'].dt.year == 2017)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Chhal 2017', width=450, height=280)


</code>
"
1136,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Puducherry, Jharkhand, and Andhra Pradesh from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Puducherry', 'Jharkhand', 'Andhra Pradesh']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Puducherry vs Jharkhand vs Andhra Pradesh', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Puducherry', 'Jharkhand', 'Andhra Pradesh']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Puducherry vs Jharkhand vs Andhra Pradesh', width=550, height=320)
     return chart


</code>
"
1137,temporal_aggregation,Show the monthly average PM10 trend for Gandhinagar from 2017 to 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Gandhinagar'].copy()
    df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Gandhinagar (2017–2022)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Gandhinagar'].copy()
     df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Gandhinagar (2017–2022)', width=600, height=300)
     return chart


</code>
"
1138,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Arunachal Pradesh, Sikkim, and Kerala from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Arunachal Pradesh', 'Sikkim', 'Kerala']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Arunachal Pradesh vs Sikkim vs Kerala', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Arunachal Pradesh', 'Sikkim', 'Kerala']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Arunachal Pradesh vs Sikkim vs Kerala', width=550, height=320)
     return chart


</code>
"
1139,spatial_aggregation,Plot the top 11 states by average PM2.5 in 2019 as a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2019]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nlargest(11, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 11 States by Average PM2.5 in 2019', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2019]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nlargest(11, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 11 States by Average PM2.5 in 2019', width=500, height=300)
     return chart


</code>
"
1140,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Rajasthan, Delhi, and Puducherry from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Rajasthan', 'Delhi', 'Puducherry']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Rajasthan vs Delhi vs Puducherry', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Rajasthan', 'Delhi', 'Puducherry']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Rajasthan vs Delhi vs Puducherry', width=550, height=320)
     return chart


</code>
"
1141,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Sikkim, Karnataka, and Arunachal Pradesh in 2021 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Sikkim', 'Karnataka', 'Arunachal Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Sikkim, Karnataka, UP – 2021', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Sikkim', 'Karnataka', 'Arunachal Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Sikkim, Karnataka, UP – 2021', width=550, height=320)
     return chart


</code>
"
1142,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Madhya Pradesh stations in 2019, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Madhya Pradesh') & (data['Timestamp'].dt.year == 2019)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Madhya Pradesh Stations 2019', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Madhya Pradesh') & (data['Timestamp'].dt.year == 2019)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Madhya Pradesh Stations 2019', width=450, height=350)


</code>
"
1143,spatial_aggregation,"Show the top 12 states by average PM10 in 2018 as a bar chart, sorted in descending order.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2018]
    df = df.groupby('state')['PM10'].mean().reset_index().dropna()
    df = df.nlargest(12, 'PM10')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Top 12 States by Average PM10 in 2018', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2018]
     df = df.groupby('state')['PM10'].mean().reset_index().dropna()
     df = df.nlargest(12, 'PM10')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Top 12 States by Average PM10 in 2018', width=500, height=300)
     return chart


</code>
"
1144,temporal_aggregation,Show the monthly average PM2.5 for Bhiwani in 2020 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Bhiwani') & (data['Timestamp'].dt.year == 2020)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Bhiwani 2020', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Bhiwani') & (data['Timestamp'].dt.year == 2020)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Bhiwani 2020', width=450, height=280)


</code>
"
1145,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Bhiwani, Chandrapur, and Dewas in 2019 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Bhiwani', 'Chandrapur', 'Dewas']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Bhiwani vs Chandrapur vs Dewas – 2019', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Bhiwani', 'Chandrapur', 'Dewas']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Bhiwani vs Chandrapur vs Dewas – 2019', width=550, height=320)
     return chart


</code>
"
1146,temporal_aggregation,Plot the monthly average PM2.5 trend for Punjab from 2017 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Punjab'].copy()
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='steelblue').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly Average PM2.5 Trend – Punjab (2017–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Punjab'].copy()
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='steelblue').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly Average PM2.5 Trend – Punjab (2017–2024)', width=600, height=300)
     return chart


</code>
"
1147,temporal_aggregation,Show a monthly bar chart of the number of days Meghalaya exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2021.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Meghalaya') & (data['Timestamp'].dt.year == 2021)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Meghalaya Exceeded WHO PM2.5 Guideline per Month – 2021', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Meghalaya') & (data['Timestamp'].dt.year == 2021)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Meghalaya Exceeded WHO PM2.5 Guideline per Month – 2021', width=500, height=300)
     return chart


</code>
"
1148,temporal_aggregation,Show a monthly bar chart of the number of days Mizoram exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2020.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Mizoram') & (data['Timestamp'].dt.year == 2020)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Mizoram Exceeded WHO PM2.5 Guideline per Month – 2020', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Mizoram') & (data['Timestamp'].dt.year == 2020)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Mizoram Exceeded WHO PM2.5 Guideline per Month – 2020', width=500, height=300)
     return chart


</code>
"
1149,temporal_aggregation,Show a monthly bar chart of the number of days Chandigarh exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2024.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Chandigarh') & (data['Timestamp'].dt.year == 2024)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Chandigarh Exceeded WHO PM2.5 Guideline per Month – 2024', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Chandigarh') & (data['Timestamp'].dt.year == 2024)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Chandigarh Exceeded WHO PM2.5 Guideline per Month – 2024', width=500, height=300)
     return chart


</code>
"
1150,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Sonipat, Solapur, and Rourkela in 2017 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Sonipat', 'Solapur', 'Rourkela']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2017)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Sonipat vs Solapur vs Rourkela – 2017', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Sonipat', 'Solapur', 'Rourkela']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2017)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Sonipat vs Solapur vs Rourkela – 2017', width=550, height=320)
     return chart


</code>
"
1151,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Puducherry, Mizoram, and Maharashtra in 2018 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Puducherry', 'Mizoram', 'Maharashtra']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2018)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Puducherry, Mizoram, UP – 2018', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Puducherry', 'Mizoram', 'Maharashtra']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2018)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Puducherry, Mizoram, UP – 2018', width=550, height=320)
     return chart


</code>
"
1152,temporal_aggregation,Plot the weekly average PM2.5 for Faridabad in 2017 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Faridabad') & (data['Timestamp'].dt.year == 2017)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Faridabad 2017', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Faridabad') & (data['Timestamp'].dt.year == 2017)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Faridabad 2017', width=600, height=300)
     return chart


</code>
"
1153,spatial_aggregation,Visualize the bottom 5 states with the lowest average PM2.5 in 2018 using a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2018]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nsmallest(5, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Bottom 5 States by Average PM2.5 in 2018', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2018]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nsmallest(5, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Bottom 5 States by Average PM2.5 in 2018', width=500, height=300)
     return chart


</code>
"
1154,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Chhattisgarh stations in 2018, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Chhattisgarh') & (data['Timestamp'].dt.year == 2018)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Chhattisgarh Stations 2018', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Chhattisgarh') & (data['Timestamp'].dt.year == 2018)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Chhattisgarh Stations 2018', width=450, height=350)


</code>
"
1155,specific_pattern,Show a cumulative area chart of PM2.5 readings for Vrindavan across 2022.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Vrindavan') & (data['Timestamp'].dt.year == 2022)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Vrindavan 2022', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Vrindavan') & (data['Timestamp'].dt.year == 2022)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Vrindavan 2022', width=600, height=300)
     return chart


</code>
"
1156,spatial_aggregation,Show a bar chart of the top 11 cities by median PM2.5 in 2024.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2024]
    df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
    df = df.nlargest(11, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
        y=alt.Y('city:N', sort='-x', title='City'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
        tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 11 Cities by Median PM2.5 in 2024', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2024]
     df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
     df = df.nlargest(11, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
         y=alt.Y('city:N', sort='-x', title='City'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
         tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 11 Cities by Median PM2.5 in 2024', width=500, height=300)
     return chart


</code>
"
1157,temporal_aggregation,Show the monthly average PM10 trend for Kanpur from 2017 to 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Kanpur'].copy()
    df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Kanpur (2017–2022)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Kanpur'].copy()
     df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Kanpur (2017–2022)', width=600, height=300)
     return chart


</code>
"
1158,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Delhi stations in 2021, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Delhi') & (data['Timestamp'].dt.year == 2021)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Delhi Stations 2021', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Delhi') & (data['Timestamp'].dt.year == 2021)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Delhi Stations 2021', width=450, height=350)


</code>
"
1159,specific_pattern,Show a cumulative area chart of PM2.5 readings for Katni across 2022.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Katni') & (data['Timestamp'].dt.year == 2022)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Katni 2022', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Katni') & (data['Timestamp'].dt.year == 2022)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Katni 2022', width=600, height=300)
     return chart


</code>
"
1160,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Haryana, Mizoram, and Manipur across 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Haryana', 'Mizoram', 'Manipur']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Haryana', 'Mizoram', 'Manipur']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
     return chart


</code>
"
1161,temporal_aggregation,Show the monthly average PM10 trend for Thrissur from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Thrissur'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Thrissur (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Thrissur'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Thrissur (2019–2024)', width=600, height=300)
     return chart


</code>
"
1162,spatial_aggregation,Plot the top 5 states by average PM2.5 in 2017 as a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2017]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nlargest(5, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 5 States by Average PM2.5 in 2017', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2017]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nlargest(5, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 5 States by Average PM2.5 in 2017', width=500, height=300)
     return chart


</code>
"
1163,temporal_aggregation,Plot the weekly average PM2.5 for Patna in 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Patna') & (data['Timestamp'].dt.year == 2024)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Patna 2024', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Patna') & (data['Timestamp'].dt.year == 2024)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Patna 2024', width=600, height=300)
     return chart


</code>
"
1164,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Jammu and Kashmir stations in 2017, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Jammu and Kashmir') & (data['Timestamp'].dt.year == 2017)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Jammu and Kashmir Stations 2017', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Jammu and Kashmir') & (data['Timestamp'].dt.year == 2017)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Jammu and Kashmir Stations 2017', width=450, height=350)


</code>
"
1165,spatial_aggregation,Plot the top 8 states by average PM2.5 in 2023 as a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2023]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nlargest(8, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 8 States by Average PM2.5 in 2023', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2023]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nlargest(8, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 8 States by Average PM2.5 in 2023', width=500, height=300)
     return chart


</code>
"
1166,spatial_aggregation,"Show the top 7 states by average PM10 in 2024 as a bar chart, sorted in descending order.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2024]
    df = df.groupby('state')['PM10'].mean().reset_index().dropna()
    df = df.nlargest(7, 'PM10')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Top 7 States by Average PM10 in 2024', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2024]
     df = df.groupby('state')['PM10'].mean().reset_index().dropna()
     df = df.nlargest(7, 'PM10')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Top 7 States by Average PM10 in 2024', width=500, height=300)
     return chart


</code>
"
1167,temporal_aggregation,Show the monthly average PM2.5 for Mandi Gobindgarh in 2019 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Mandi Gobindgarh') & (data['Timestamp'].dt.year == 2019)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Mandi Gobindgarh 2019', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Mandi Gobindgarh') & (data['Timestamp'].dt.year == 2019)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Mandi Gobindgarh 2019', width=450, height=280)


</code>
"
1168,specific_pattern,Plot the rolling 30-day average PM2.5 for Uttarakhand in 2020 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Uttarakhand') & (data['Timestamp'].dt.year == 2020)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Uttarakhand 2020', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Uttarakhand') & (data['Timestamp'].dt.year == 2020)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Uttarakhand 2020', width=600, height=300)


</code>
"
1169,temporal_aggregation,Show the monthly average PM10 trend for Dausa from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Dausa'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Dausa (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Dausa'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Dausa (2019–2024)', width=600, height=300)
     return chart


</code>
"
1170,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Arunachal Pradesh stations in 2023, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Arunachal Pradesh') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Arunachal Pradesh Stations 2023', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Arunachal Pradesh') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Arunachal Pradesh Stations 2023', width=450, height=350)


</code>
"
1171,specific_pattern,Plot the rolling 30-day average PM2.5 for Meghalaya in 2019 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Meghalaya') & (data['Timestamp'].dt.year == 2019)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Meghalaya 2019', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Meghalaya') & (data['Timestamp'].dt.year == 2019)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Meghalaya 2019', width=600, height=300)


</code>
"
1172,temporal_aggregation,Show a monthly bar chart of the number of days Nagaland exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2022.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Nagaland') & (data['Timestamp'].dt.year == 2022)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Nagaland Exceeded WHO PM2.5 Guideline per Month – 2022', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Nagaland') & (data['Timestamp'].dt.year == 2022)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Nagaland Exceeded WHO PM2.5 Guideline per Month – 2022', width=500, height=300)
     return chart


</code>
"
1173,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Telangana, Madhya Pradesh, and West Bengal in 2019 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Telangana', 'Madhya Pradesh', 'West Bengal']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Telangana, Madhya Pradesh, UP – 2019', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Telangana', 'Madhya Pradesh', 'West Bengal']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Telangana, Madhya Pradesh, UP – 2019', width=550, height=320)
     return chart


</code>
"
1174,spatio_temporal_aggregation,"Create a faceted bar chart showing top 13 states by average PM2.5 per year for 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year.isin([2019,2020,2021,2022])].copy()
    df['Year'] = df['Timestamp'].dt.year
    agg = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    top = agg.groupby('Year').apply(lambda x: x.nlargest(13,'PM2.5')).reset_index(drop=True)
    chart = alt.Chart(top).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Avg PM2.5'),
        y=alt.Y('state:N', sort='-x'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).facet(facet='Year:O', columns=2).properties(title='Top 13 States by PM2.5 per Year')
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year.isin([2019,2020,2021,2022])].copy()
     df['Year'] = df['Timestamp'].dt.year
     agg = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     top = agg.groupby('Year').apply(lambda x: x.nlargest(13,'PM2.5')).reset_index(drop=True)
     chart = alt.Chart(top).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Avg PM2.5'),
         y=alt.Y('state:N', sort='-x'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).facet(facet='Year:O', columns=2).properties(title='Top 13 States by PM2.5 per Year')
     return chart


</code>
"
1175,temporal_aggregation,Show the monthly average PM2.5 for Rajsamand in 2017 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Rajsamand') & (data['Timestamp'].dt.year == 2017)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Rajsamand 2017', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Rajsamand') & (data['Timestamp'].dt.year == 2017)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Rajsamand 2017', width=450, height=280)


</code>
"
1176,temporal_aggregation,Show the monthly average PM2.5 for Silchar in 2019 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Silchar') & (data['Timestamp'].dt.year == 2019)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Silchar 2019', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Silchar') & (data['Timestamp'].dt.year == 2019)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Silchar 2019', width=450, height=280)


</code>
"
1177,specific_pattern,Plot the rolling 30-day average PM2.5 for Delhi in 2018 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Delhi') & (data['Timestamp'].dt.year == 2018)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Delhi 2018', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Delhi') & (data['Timestamp'].dt.year == 2018)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Delhi 2018', width=600, height=300)


</code>
"
1178,spatial_aggregation,Visualize the bottom 11 states with the lowest average PM2.5 in 2020 using a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2020]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nsmallest(11, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Bottom 11 States by Average PM2.5 in 2020', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2020]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nsmallest(11, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Bottom 11 States by Average PM2.5 in 2020', width=500, height=300)
     return chart


</code>
"
1179,spatial_aggregation,Plot the top 5 states by average PM2.5 in 2019 as a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2019]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nlargest(5, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 5 States by Average PM2.5 in 2019', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2019]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nlargest(5, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 5 States by Average PM2.5 in 2019', width=500, height=300)
     return chart


</code>
"
1180,temporal_aggregation,Show the monthly average PM2.5 for Katihar in 2018 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Katihar') & (data['Timestamp'].dt.year == 2018)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Katihar 2018', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Katihar') & (data['Timestamp'].dt.year == 2018)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Katihar 2018', width=450, height=280)


</code>
"
1181,spatial_aggregation,Plot the top 13 states by average PM2.5 in 2022 as a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2022]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nlargest(13, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 13 States by Average PM2.5 in 2022', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2022]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nlargest(13, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 13 States by Average PM2.5 in 2022', width=500, height=300)
     return chart


</code>
"
1182,temporal_aggregation,Plot the weekly average PM2.5 for Meerut in 2021 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Meerut') & (data['Timestamp'].dt.year == 2021)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Meerut 2021', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Meerut') & (data['Timestamp'].dt.year == 2021)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Meerut 2021', width=600, height=300)
     return chart


</code>
"
1183,temporal_aggregation,Show the monthly average PM10 trend for Sasaram from 2017 to 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Sasaram'].copy()
    df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Sasaram (2017–2022)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Sasaram'].copy()
     df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Sasaram (2017–2022)', width=600, height=300)
     return chart


</code>
"
1184,specific_pattern,Plot the rolling 30-day average PM2.5 for Bihar in 2017 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Bihar') & (data['Timestamp'].dt.year == 2017)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Bihar 2017', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Bihar') & (data['Timestamp'].dt.year == 2017)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Bihar 2017', width=600, height=300)


</code>
"
1185,specific_pattern,Show a cumulative area chart of PM2.5 readings for Ramanagara across 2023.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Ramanagara') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Ramanagara 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Ramanagara') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Ramanagara 2023', width=600, height=300)
     return chart


</code>
"
1186,specific_pattern,Show a cumulative area chart of PM2.5 readings for Bidar across 2021.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Bidar') & (data['Timestamp'].dt.year == 2021)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Bidar 2021', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Bidar') & (data['Timestamp'].dt.year == 2021)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Bidar 2021', width=600, height=300)
     return chart


</code>
"
1187,specific_pattern,Plot the rolling 30-day average PM2.5 for Punjab in 2023 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Punjab') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Punjab 2023', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Punjab') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Punjab 2023', width=600, height=300)


</code>
"
1188,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for West Bengal, Chandigarh, and Gujarat from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['West Bengal', 'Chandigarh', 'Gujarat']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: West Bengal vs Chandigarh vs Gujarat', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['West Bengal', 'Chandigarh', 'Gujarat']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: West Bengal vs Chandigarh vs Gujarat', width=550, height=320)
     return chart


</code>
"
1189,spatial_aggregation,Show a bar chart of the top 6 cities by median PM2.5 in 2020.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2020]
    df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
    df = df.nlargest(6, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
        y=alt.Y('city:N', sort='-x', title='City'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
        tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 6 Cities by Median PM2.5 in 2020', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2020]
     df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
     df = df.nlargest(6, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
         y=alt.Y('city:N', sort='-x', title='City'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
         tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 6 Cities by Median PM2.5 in 2020', width=500, height=300)
     return chart


</code>
"
1190,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Meghalaya, Gujarat, and Chhattisgarh in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Meghalaya', 'Gujarat', 'Chhattisgarh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Meghalaya, Gujarat, UP – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Meghalaya', 'Gujarat', 'Chhattisgarh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Meghalaya, Gujarat, UP – 2022', width=550, height=320)
     return chart


</code>
"
1191,specific_pattern,Show a cumulative area chart of PM2.5 readings for Pune across 2022.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Pune') & (data['Timestamp'].dt.year == 2022)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Pune 2022', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Pune') & (data['Timestamp'].dt.year == 2022)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Pune 2022', width=600, height=300)
     return chart


</code>
"
1192,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Meghalaya, Gujarat, and Punjab across 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Meghalaya', 'Gujarat', 'Punjab']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Meghalaya', 'Gujarat', 'Punjab']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
     return chart


</code>
"
1193,spatial_aggregation,Show a bar chart of the top 14 cities by median PM2.5 in 2023.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2023]
    df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
    df = df.nlargest(14, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
        y=alt.Y('city:N', sort='-x', title='City'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
        tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 14 Cities by Median PM2.5 in 2023', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2023]
     df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
     df = df.nlargest(14, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
         y=alt.Y('city:N', sort='-x', title='City'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
         tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 14 Cities by Median PM2.5 in 2023', width=500, height=300)
     return chart


</code>
"
1194,specific_pattern,Show PM2.5 exceedances above the Indian standard (60 µg/m³) per year as a bar chart for Arunachal Pradesh.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Arunachal Pradesh'].dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 60].copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby('Year')['Timestamp'].nunique().reset_index()
    df.columns = ['Year','Days Exceeded']
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('Days Exceeded:Q', title='Days PM2.5 > 60 µg/m³'),
        tooltip=['Year:N','Days Exceeded:Q']
    ).properties(title='Days Arunachal Pradesh Exceeded Indian PM2.5 Standard (60 µg/m³) per Year', width=450, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Arunachal Pradesh'].dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 60].copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby('Year')['Timestamp'].nunique().reset_index()
     df.columns = ['Year','Days Exceeded']
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('Days Exceeded:Q', title='Days PM2.5 > 60 µg/m³'),
         tooltip=['Year:N','Days Exceeded:Q']
     ).properties(title='Days Arunachal Pradesh Exceeded Indian PM2.5 Standard (60 µg/m³) per Year', width=450, height=300)
     return chart


</code>
"
1195,temporal_aggregation,Show the monthly average PM2.5 for Kunjemura in 2018 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Kunjemura') & (data['Timestamp'].dt.year == 2018)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Kunjemura 2018', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Kunjemura') & (data['Timestamp'].dt.year == 2018)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Kunjemura 2018', width=450, height=280)


</code>
"
1196,temporal_aggregation,Show the monthly average PM10 trend for Baripada from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Baripada'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Baripada (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Baripada'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Baripada (2019–2024)', width=600, height=300)
     return chart


</code>
"
1197,spatial_aggregation,"Show the top 11 states by average PM10 in 2018 as a bar chart, sorted in descending order.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2018]
    df = df.groupby('state')['PM10'].mean().reset_index().dropna()
    df = df.nlargest(11, 'PM10')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Top 11 States by Average PM10 in 2018', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2018]
     df = df.groupby('state')['PM10'].mean().reset_index().dropna()
     df = df.nlargest(11, 'PM10')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Top 11 States by Average PM10 in 2018', width=500, height=300)
     return chart


</code>
"
1198,specific_pattern,Show a cumulative area chart of PM2.5 readings for Noida across 2022.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Noida') & (data['Timestamp'].dt.year == 2022)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Noida 2022', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Noida') & (data['Timestamp'].dt.year == 2022)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Noida 2022', width=600, height=300)
     return chart


</code>
"
1199,temporal_aggregation,Show the monthly average PM2.5 for Nandesari in 2022 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Nandesari') & (data['Timestamp'].dt.year == 2022)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Nandesari 2022', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Nandesari') & (data['Timestamp'].dt.year == 2022)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Nandesari 2022', width=450, height=280)


</code>
"
1200,temporal_aggregation,Show the monthly average PM10 trend for Chikkamagaluru from 2017 to 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Chikkamagaluru'].copy()
    df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Chikkamagaluru (2017–2022)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Chikkamagaluru'].copy()
     df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Chikkamagaluru (2017–2022)', width=600, height=300)
     return chart


</code>
"
1201,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Madhya Pradesh stations in 2024, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Madhya Pradesh') & (data['Timestamp'].dt.year == 2024)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Madhya Pradesh Stations 2024', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Madhya Pradesh') & (data['Timestamp'].dt.year == 2024)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Madhya Pradesh Stations 2024', width=450, height=350)


</code>
"
1202,specific_pattern,Show a cumulative area chart of PM2.5 readings for Prayagraj across 2022.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Prayagraj') & (data['Timestamp'].dt.year == 2022)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Prayagraj 2022', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Prayagraj') & (data['Timestamp'].dt.year == 2022)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Prayagraj 2022', width=600, height=300)
     return chart


</code>
"
1203,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Jharkhand, Gujarat, and Jharkhand across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Jharkhand', 'Gujarat', 'Jharkhand']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Jharkhand', 'Gujarat', 'Jharkhand']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
1204,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Mizoram, Tripura, and Tripura across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Mizoram', 'Tripura', 'Tripura']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Mizoram', 'Tripura', 'Tripura']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
1205,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Haryana, Manipur, and Kerala in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Haryana', 'Manipur', 'Kerala']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Haryana, Manipur, UP – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Haryana', 'Manipur', 'Kerala']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Haryana, Manipur, UP – 2023', width=550, height=320)
     return chart


</code>
"
1206,spatial_aggregation,Visualize the bottom 14 states with the lowest average PM2.5 in 2017 using a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2017]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nsmallest(14, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Bottom 14 States by Average PM2.5 in 2017', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2017]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nsmallest(14, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Bottom 14 States by Average PM2.5 in 2017', width=500, height=300)
     return chart


</code>
"
1207,spatial_aggregation,Plot the top 12 states by average PM2.5 in 2021 as a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2021]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nlargest(12, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 12 States by Average PM2.5 in 2021', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2021]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nlargest(12, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 12 States by Average PM2.5 in 2021', width=500, height=300)
     return chart


</code>
"
1208,temporal_aggregation,Show a monthly bar chart of the number of days Punjab exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2017.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Punjab') & (data['Timestamp'].dt.year == 2017)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Punjab Exceeded WHO PM2.5 Guideline per Month – 2017', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Punjab') & (data['Timestamp'].dt.year == 2017)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Punjab Exceeded WHO PM2.5 Guideline per Month – 2017', width=500, height=300)
     return chart


</code>
"
1209,temporal_aggregation,Show the monthly average PM10 trend for Kanpur from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Kanpur'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Kanpur (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Kanpur'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Kanpur (2019–2024)', width=600, height=300)
     return chart


</code>
"
1210,specific_pattern,Show PM2.5 exceedances above the Indian standard (60 µg/m³) per year as a bar chart for Madhya Pradesh.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Madhya Pradesh'].dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 60].copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby('Year')['Timestamp'].nunique().reset_index()
    df.columns = ['Year','Days Exceeded']
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('Days Exceeded:Q', title='Days PM2.5 > 60 µg/m³'),
        tooltip=['Year:N','Days Exceeded:Q']
    ).properties(title='Days Madhya Pradesh Exceeded Indian PM2.5 Standard (60 µg/m³) per Year', width=450, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Madhya Pradesh'].dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 60].copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby('Year')['Timestamp'].nunique().reset_index()
     df.columns = ['Year','Days Exceeded']
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('Days Exceeded:Q', title='Days PM2.5 > 60 µg/m³'),
         tooltip=['Year:N','Days Exceeded:Q']
     ).properties(title='Days Madhya Pradesh Exceeded Indian PM2.5 Standard (60 µg/m³) per Year', width=450, height=300)
     return chart


</code>
"
1211,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Eloor, Siliguri, and Indore in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Eloor', 'Siliguri', 'Indore']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Eloor vs Siliguri vs Indore – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Eloor', 'Siliguri', 'Indore']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Eloor vs Siliguri vs Indore – 2024', width=550, height=320)
     return chart


</code>
"
1212,spatial_aggregation,Visualize the bottom 6 states with the lowest average PM2.5 in 2019 using a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2019]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nsmallest(6, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Bottom 6 States by Average PM2.5 in 2019', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2019]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nsmallest(6, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Bottom 6 States by Average PM2.5 in 2019', width=500, height=300)
     return chart


</code>
"
1213,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Karnataka stations in 2024, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Karnataka') & (data['Timestamp'].dt.year == 2024)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Karnataka Stations 2024', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Karnataka') & (data['Timestamp'].dt.year == 2024)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Karnataka Stations 2024', width=450, height=350)


</code>
"
1214,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Rajasthan, Madhya Pradesh, and Odisha in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Rajasthan', 'Madhya Pradesh', 'Odisha']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Rajasthan, Madhya Pradesh, UP – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Rajasthan', 'Madhya Pradesh', 'Odisha']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Rajasthan, Madhya Pradesh, UP – 2023', width=550, height=320)
     return chart


</code>
"
1215,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Dharuhera, Ballabgarh, and Byasanagar in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Dharuhera', 'Ballabgarh', 'Byasanagar']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Dharuhera vs Ballabgarh vs Byasanagar – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Dharuhera', 'Ballabgarh', 'Byasanagar']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Dharuhera vs Ballabgarh vs Byasanagar – 2024', width=550, height=320)
     return chart


</code>
"
1216,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Karnataka, Punjab, and Puducherry from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Karnataka', 'Punjab', 'Puducherry']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Karnataka vs Punjab vs Puducherry', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Karnataka', 'Punjab', 'Puducherry']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Karnataka vs Punjab vs Puducherry', width=550, height=320)
     return chart


</code>
"
1217,spatio_temporal_aggregation,Show the monthly average PM2.5 for Uttarakhand across each year from 2017 to 2024 as small multiples (faceted by year).,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Uttarakhand'].copy()
    df['Year'] = df['Timestamp'].dt.year
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='steelblue').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5'),
        tooltip=['Year:O','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).facet(
        facet=alt.Facet('Year:O', title='Year'),
        columns=4
    ).properties(title='Monthly PM2.5 in Uttarakhand by Year (2017–2024)')
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Uttarakhand'].copy()
     df['Year'] = df['Timestamp'].dt.year
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='steelblue').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5'),
         tooltip=['Year:O','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).facet(
         facet=alt.Facet('Year:O', title='Year'),
         columns=4
     ).properties(title='Monthly PM2.5 in Uttarakhand by Year (2017–2024)')
     return chart


</code>
"
1218,specific_pattern,Plot the rolling 30-day average PM2.5 for West Bengal in 2019 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'West Bengal') & (data['Timestamp'].dt.year == 2019)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – West Bengal 2019', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'West Bengal') & (data['Timestamp'].dt.year == 2019)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – West Bengal 2019', width=600, height=300)


</code>
"
1219,spatial_aggregation,Visualize the bottom 13 states with the lowest average PM2.5 in 2017 using a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2017]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nsmallest(13, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Bottom 13 States by Average PM2.5 in 2017', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2017]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nsmallest(13, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Bottom 13 States by Average PM2.5 in 2017', width=500, height=300)
     return chart


</code>
"
1220,spatial_aggregation,Plot the distribution of PM2.5 values in Jharkhand across all years using a histogram.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Jharkhand'].dropna(subset=['PM2.5'])
    df = df[['PM2.5']]
    chart = alt.Chart(df).mark_bar(color='steelblue', opacity=0.75).encode(
        x=alt.X('PM2\.5:Q', bin=alt.Bin(maxbins=40), title='PM2.5 (µg/m³)'),
        y=alt.Y('count()', title='Number of Observations'),
        tooltip=[alt.Tooltip('PM2\.5:Q', bin=True), 'count()']
    ).properties(title='PM2.5 Distribution – Jharkhand (All Years)', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Jharkhand'].dropna(subset=['PM2.5'])
     df = df[['PM2.5']]
     chart = alt.Chart(df).mark_bar(color='steelblue', opacity=0.75).encode(
         x=alt.X('PM2\.5:Q', bin=alt.Bin(maxbins=40), title='PM2.5 (µg/m³)'),
         y=alt.Y('count()', title='Number of Observations'),
         tooltip=[alt.Tooltip('PM2\.5:Q', bin=True), 'count()']
     ).properties(title='PM2.5 Distribution – Jharkhand (All Years)', width=500, height=300)
     return chart


</code>
"
1221,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Jammu and Kashmir, Tamil Nadu, and Haryana across 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Jammu and Kashmir', 'Tamil Nadu', 'Haryana']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Jammu and Kashmir', 'Tamil Nadu', 'Haryana']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
     return chart


</code>
"
1222,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Mizoram, Puducherry, and Sikkim in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Mizoram', 'Puducherry', 'Sikkim']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Mizoram, Puducherry, UP – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Mizoram', 'Puducherry', 'Sikkim']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Mizoram, Puducherry, UP – 2023', width=550, height=320)
     return chart


</code>
"
1223,temporal_aggregation,Show the monthly average PM2.5 for Malegaon in 2022 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Malegaon') & (data['Timestamp'].dt.year == 2022)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Malegaon 2022', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Malegaon') & (data['Timestamp'].dt.year == 2022)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Malegaon 2022', width=450, height=280)


</code>
"
1224,specific_pattern,Plot the rolling 30-day average PM2.5 for Chhattisgarh in 2023 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Chhattisgarh') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Chhattisgarh 2023', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Chhattisgarh') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Chhattisgarh 2023', width=600, height=300)


</code>
"
1225,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Rajasthan, Haryana, and Himachal Pradesh from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Rajasthan', 'Haryana', 'Himachal Pradesh']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Rajasthan vs Haryana vs Himachal Pradesh', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Rajasthan', 'Haryana', 'Himachal Pradesh']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Rajasthan vs Haryana vs Himachal Pradesh', width=550, height=320)
     return chart


</code>
"
1226,temporal_aggregation,Show the monthly average PM2.5 for Anantapur in 2017 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Anantapur') & (data['Timestamp'].dt.year == 2017)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Anantapur 2017', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Anantapur') & (data['Timestamp'].dt.year == 2017)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Anantapur 2017', width=450, height=280)


</code>
"
1227,specific_pattern,Show PM2.5 exceedances above the Indian standard (60 µg/m³) per year as a bar chart for Uttar Pradesh.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Uttar Pradesh'].dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 60].copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby('Year')['Timestamp'].nunique().reset_index()
    df.columns = ['Year','Days Exceeded']
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('Days Exceeded:Q', title='Days PM2.5 > 60 µg/m³'),
        tooltip=['Year:N','Days Exceeded:Q']
    ).properties(title='Days Uttar Pradesh Exceeded Indian PM2.5 Standard (60 µg/m³) per Year', width=450, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Uttar Pradesh'].dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 60].copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby('Year')['Timestamp'].nunique().reset_index()
     df.columns = ['Year','Days Exceeded']
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('Days Exceeded:Q', title='Days PM2.5 > 60 µg/m³'),
         tooltip=['Year:N','Days Exceeded:Q']
     ).properties(title='Days Uttar Pradesh Exceeded Indian PM2.5 Standard (60 µg/m³) per Year', width=450, height=300)
     return chart


</code>
"
1228,temporal_aggregation,Plot the weekly average PM2.5 for Chandigarh in 2021 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Chandigarh') & (data['Timestamp'].dt.year == 2021)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Chandigarh 2021', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Chandigarh') & (data['Timestamp'].dt.year == 2021)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Chandigarh 2021', width=600, height=300)
     return chart


</code>
"
1229,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Rishikesh, Bengaluru, and Samastipur in 2019 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Rishikesh', 'Bengaluru', 'Samastipur']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Rishikesh vs Bengaluru vs Samastipur – 2019', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Rishikesh', 'Bengaluru', 'Samastipur']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Rishikesh vs Bengaluru vs Samastipur – 2019', width=550, height=320)
     return chart


</code>
"
1230,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Rajamahendravaram, Durgapur, and Kaithal in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Rajamahendravaram', 'Durgapur', 'Kaithal']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Rajamahendravaram vs Durgapur vs Kaithal – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Rajamahendravaram', 'Durgapur', 'Kaithal']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Rajamahendravaram vs Durgapur vs Kaithal – 2024', width=550, height=320)
     return chart


</code>
"
1231,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Solapur, Ambala, and Ramanagara in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Solapur', 'Ambala', 'Ramanagara']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Solapur vs Ambala vs Ramanagara – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Solapur', 'Ambala', 'Ramanagara']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Solapur vs Ambala vs Ramanagara – 2024', width=550, height=320)
     return chart


</code>
"
1232,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Tripura, Sikkim, and Rajasthan across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Tripura', 'Sikkim', 'Rajasthan']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Tripura', 'Sikkim', 'Rajasthan']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
1233,specific_pattern,Plot the rolling 30-day average PM2.5 for Karnataka in 2019 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Karnataka') & (data['Timestamp'].dt.year == 2019)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Karnataka 2019', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Karnataka') & (data['Timestamp'].dt.year == 2019)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Karnataka 2019', width=600, height=300)


</code>
"
1234,temporal_aggregation,Show the monthly average PM2.5 for Vijayawada in 2018 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Vijayawada') & (data['Timestamp'].dt.year == 2018)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Vijayawada 2018', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Vijayawada') & (data['Timestamp'].dt.year == 2018)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Vijayawada 2018', width=450, height=280)


</code>
"
1235,temporal_aggregation,Show the monthly average PM2.5 for Navi Mumbai in 2019 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Navi Mumbai') & (data['Timestamp'].dt.year == 2019)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Navi Mumbai 2019', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Navi Mumbai') & (data['Timestamp'].dt.year == 2019)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Navi Mumbai 2019', width=450, height=280)


</code>
"
1236,specific_pattern,Show a cumulative area chart of PM2.5 readings for Singrauli across 2019.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Singrauli') & (data['Timestamp'].dt.year == 2019)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Singrauli 2019', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Singrauli') & (data['Timestamp'].dt.year == 2019)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Singrauli 2019', width=600, height=300)
     return chart


</code>
"
1237,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Assam, Karnataka, and Tamil Nadu in 2021 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Assam', 'Karnataka', 'Tamil Nadu']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Assam, Karnataka, UP – 2021', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Assam', 'Karnataka', 'Tamil Nadu']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Assam, Karnataka, UP – 2021', width=550, height=320)
     return chart


</code>
"
1238,spatial_aggregation,Visualize the bottom 6 states with the lowest average PM2.5 in 2020 using a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2020]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nsmallest(6, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Bottom 6 States by Average PM2.5 in 2020', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2020]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nsmallest(6, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Bottom 6 States by Average PM2.5 in 2020', width=500, height=300)
     return chart


</code>
"
1239,temporal_aggregation,Plot the weekly average PM2.5 for Tiruchirappalli in 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Tiruchirappalli') & (data['Timestamp'].dt.year == 2024)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Tiruchirappalli 2024', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Tiruchirappalli') & (data['Timestamp'].dt.year == 2024)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Tiruchirappalli 2024', width=600, height=300)
     return chart


</code>
"
1240,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Uttarakhand, Arunachal Pradesh, and Assam from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Uttarakhand', 'Arunachal Pradesh', 'Assam']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Uttarakhand vs Arunachal Pradesh vs Assam', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Uttarakhand', 'Arunachal Pradesh', 'Assam']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Uttarakhand vs Arunachal Pradesh vs Assam', width=550, height=320)
     return chart


</code>
"
1241,spatial_aggregation,"Show the top 14 states by average PM10 in 2021 as a bar chart, sorted in descending order.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2021]
    df = df.groupby('state')['PM10'].mean().reset_index().dropna()
    df = df.nlargest(14, 'PM10')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Top 14 States by Average PM10 in 2021', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2021]
     df = df.groupby('state')['PM10'].mean().reset_index().dropna()
     df = df.nlargest(14, 'PM10')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Top 14 States by Average PM10 in 2021', width=500, height=300)
     return chart


</code>
"
1242,spatial_aggregation,Visualize the bottom 15 states with the lowest average PM2.5 in 2018 using a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2018]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nsmallest(15, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Bottom 15 States by Average PM2.5 in 2018', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2018]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nsmallest(15, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Bottom 15 States by Average PM2.5 in 2018', width=500, height=300)
     return chart


</code>
"
1243,spatio_temporal_aggregation,"Create a faceted bar chart showing top 12 states by average PM2.5 per year for 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year.isin([2017,2018,2019,2020])].copy()
    df['Year'] = df['Timestamp'].dt.year
    agg = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    top = agg.groupby('Year').apply(lambda x: x.nlargest(12,'PM2.5')).reset_index(drop=True)
    chart = alt.Chart(top).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Avg PM2.5'),
        y=alt.Y('state:N', sort='-x'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).facet(facet='Year:O', columns=2).properties(title='Top 12 States by PM2.5 per Year')
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year.isin([2017,2018,2019,2020])].copy()
     df['Year'] = df['Timestamp'].dt.year
     agg = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     top = agg.groupby('Year').apply(lambda x: x.nlargest(12,'PM2.5')).reset_index(drop=True)
     chart = alt.Chart(top).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Avg PM2.5'),
         y=alt.Y('state:N', sort='-x'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).facet(facet='Year:O', columns=2).properties(title='Top 12 States by PM2.5 per Year')
     return chart


</code>
"
1244,spatial_aggregation,Show a bar chart of the top 8 cities by median PM2.5 in 2024.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2024]
    df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
    df = df.nlargest(8, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
        y=alt.Y('city:N', sort='-x', title='City'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
        tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 8 Cities by Median PM2.5 in 2024', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2024]
     df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
     df = df.nlargest(8, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
         y=alt.Y('city:N', sort='-x', title='City'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
         tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 8 Cities by Median PM2.5 in 2024', width=500, height=300)
     return chart


</code>
"
1245,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Tamil Nadu, Rajasthan, and Telangana across 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Tamil Nadu', 'Rajasthan', 'Telangana']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Tamil Nadu', 'Rajasthan', 'Telangana']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
     return chart


</code>
"
1246,specific_pattern,Plot the rolling 30-day average PM2.5 for Jammu and Kashmir in 2020 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Jammu and Kashmir') & (data['Timestamp'].dt.year == 2020)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Jammu and Kashmir 2020', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Jammu and Kashmir') & (data['Timestamp'].dt.year == 2020)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Jammu and Kashmir 2020', width=600, height=300)


</code>
"
1247,temporal_aggregation,Show the monthly average PM10 trend for Haldia from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Haldia'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Haldia (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Haldia'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Haldia (2019–2024)', width=600, height=300)
     return chart


</code>
"
1248,spatial_aggregation,Visualize the bottom 7 states with the lowest average PM2.5 in 2022 using a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2022]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nsmallest(7, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Bottom 7 States by Average PM2.5 in 2022', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2022]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nsmallest(7, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Bottom 7 States by Average PM2.5 in 2022', width=500, height=300)
     return chart


</code>
"
1249,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Punjab stations in 2018, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Punjab') & (data['Timestamp'].dt.year == 2018)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Punjab Stations 2018', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Punjab') & (data['Timestamp'].dt.year == 2018)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Punjab Stations 2018', width=450, height=350)


</code>
"
1250,spatio_temporal_aggregation,Show a heatmap of average PM2.5 for the top 7 most polluted states by month for 2022.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    top8 = data.groupby('state')['PM2.5'].mean().nlargest(7).index.tolist()
    df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_rect().encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('state:N', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
        tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Heatmap – Top 7 Polluted States by Month (2022)', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     top8 = data.groupby('state')['PM2.5'].mean().nlargest(7).index.tolist()
     df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_rect().encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('state:N', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
         tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Heatmap – Top 7 Polluted States by Month (2022)', width=500, height=300)
     return chart


</code>
"
1251,temporal_aggregation,Show the monthly average PM10 trend for Nagpur from 2017 to 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Nagpur'].copy()
    df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Nagpur (2017–2022)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Nagpur'].copy()
     df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Nagpur (2017–2022)', width=600, height=300)
     return chart


</code>
"
1252,temporal_aggregation,Show a monthly bar chart of the number of days Uttar Pradesh exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2017.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Uttar Pradesh') & (data['Timestamp'].dt.year == 2017)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Uttar Pradesh Exceeded WHO PM2.5 Guideline per Month – 2017', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Uttar Pradesh') & (data['Timestamp'].dt.year == 2017)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Uttar Pradesh Exceeded WHO PM2.5 Guideline per Month – 2017', width=500, height=300)
     return chart


</code>
"
1253,spatial_aggregation,Plot the top 7 states by average PM2.5 in 2017 as a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2017]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nlargest(7, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 7 States by Average PM2.5 in 2017', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2017]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nlargest(7, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 7 States by Average PM2.5 in 2017', width=500, height=300)
     return chart


</code>
"
1254,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Tamil Nadu stations in 2023, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Tamil Nadu') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Tamil Nadu Stations 2023', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Tamil Nadu') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Tamil Nadu Stations 2023', width=450, height=350)


</code>
"
1255,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for West Bengal stations in 2018, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'West Bengal') & (data['Timestamp'].dt.year == 2018)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – West Bengal Stations 2018', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'West Bengal') & (data['Timestamp'].dt.year == 2018)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – West Bengal Stations 2018', width=450, height=350)


</code>
"
1256,temporal_aggregation,Show the monthly average PM2.5 for Tonk in 2017 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Tonk') & (data['Timestamp'].dt.year == 2017)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Tonk 2017', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Tonk') & (data['Timestamp'].dt.year == 2017)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Tonk 2017', width=450, height=280)


</code>
"
1257,spatio_temporal_aggregation,Show a heatmap of average PM2.5 for the top 13 most polluted states by month for 2024.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    top8 = data.groupby('state')['PM2.5'].mean().nlargest(13).index.tolist()
    df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_rect().encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('state:N', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
        tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Heatmap – Top 13 Polluted States by Month (2024)', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     top8 = data.groupby('state')['PM2.5'].mean().nlargest(13).index.tolist()
     df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_rect().encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('state:N', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
         tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Heatmap – Top 13 Polluted States by Month (2024)', width=500, height=300)
     return chart


</code>
"
1258,temporal_aggregation,Show the monthly average PM2.5 for Cuttack in 2017 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Cuttack') & (data['Timestamp'].dt.year == 2017)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Cuttack 2017', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Cuttack') & (data['Timestamp'].dt.year == 2017)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Cuttack 2017', width=450, height=280)


</code>
"
1259,specific_pattern,Plot the rolling 30-day average PM2.5 for West Bengal in 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'West Bengal') & (data['Timestamp'].dt.year == 2024)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – West Bengal 2024', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'West Bengal') & (data['Timestamp'].dt.year == 2024)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – West Bengal 2024', width=600, height=300)


</code>
"
1260,temporal_aggregation,Show a monthly bar chart of the number of days Uttarakhand exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2023.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Uttarakhand') & (data['Timestamp'].dt.year == 2023)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Uttarakhand Exceeded WHO PM2.5 Guideline per Month – 2023', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Uttarakhand') & (data['Timestamp'].dt.year == 2023)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Uttarakhand Exceeded WHO PM2.5 Guideline per Month – 2023', width=500, height=300)
     return chart


</code>
"
1261,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Jammu and Kashmir, Tamil Nadu, and Nagaland across 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Jammu and Kashmir', 'Tamil Nadu', 'Nagaland']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Jammu and Kashmir', 'Tamil Nadu', 'Nagaland']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
     return chart


</code>
"
1262,temporal_aggregation,Show a monthly bar chart of the number of days Andhra Pradesh exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2024.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Andhra Pradesh') & (data['Timestamp'].dt.year == 2024)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Andhra Pradesh Exceeded WHO PM2.5 Guideline per Month – 2024', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Andhra Pradesh') & (data['Timestamp'].dt.year == 2024)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Andhra Pradesh Exceeded WHO PM2.5 Guideline per Month – 2024', width=500, height=300)
     return chart


</code>
"
1263,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Mizoram, Madhya Pradesh, and Jammu and Kashmir in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Mizoram', 'Madhya Pradesh', 'Jammu and Kashmir']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Mizoram, Madhya Pradesh, UP – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Mizoram', 'Madhya Pradesh', 'Jammu and Kashmir']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Mizoram, Madhya Pradesh, UP – 2023', width=550, height=320)
     return chart


</code>
"
1264,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Rajasthan stations in 2018, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Rajasthan') & (data['Timestamp'].dt.year == 2018)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Rajasthan Stations 2018', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Rajasthan') & (data['Timestamp'].dt.year == 2018)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Rajasthan Stations 2018', width=450, height=350)


</code>
"
1265,temporal_aggregation,Plot the monthly average PM2.5 trend for Jammu and Kashmir from 2017 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Jammu and Kashmir'].copy()
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='steelblue').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly Average PM2.5 Trend – Jammu and Kashmir (2017–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Jammu and Kashmir'].copy()
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='steelblue').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly Average PM2.5 Trend – Jammu and Kashmir (2017–2024)', width=600, height=300)
     return chart


</code>
"
1266,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Bihar, Rajasthan, and Gujarat from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Bihar', 'Rajasthan', 'Gujarat']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Bihar vs Rajasthan vs Gujarat', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Bihar', 'Rajasthan', 'Gujarat']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Bihar vs Rajasthan vs Gujarat', width=550, height=320)
     return chart


</code>
"
1267,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Delhi stations in 2024, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Delhi') & (data['Timestamp'].dt.year == 2024)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Delhi Stations 2024', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Delhi') & (data['Timestamp'].dt.year == 2024)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Delhi Stations 2024', width=450, height=350)


</code>
"
1268,spatial_aggregation,Show a bar chart of the top 14 cities by median PM2.5 in 2020.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2020]
    df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
    df = df.nlargest(14, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
        y=alt.Y('city:N', sort='-x', title='City'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
        tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 14 Cities by Median PM2.5 in 2020', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2020]
     df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
     df = df.nlargest(14, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
         y=alt.Y('city:N', sort='-x', title='City'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
         tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 14 Cities by Median PM2.5 in 2020', width=500, height=300)
     return chart


</code>
"
1269,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Delhi, Nagaland, and Uttarakhand across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Delhi', 'Nagaland', 'Uttarakhand']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Delhi', 'Nagaland', 'Uttarakhand']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
1270,temporal_aggregation,Show the monthly average PM10 trend for Gangtok from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Gangtok'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Gangtok (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Gangtok'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Gangtok (2019–2024)', width=600, height=300)
     return chart


</code>
"
1271,spatial_aggregation,Visualize the bottom 14 states with the lowest average PM2.5 in 2023 using a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2023]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nsmallest(14, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Bottom 14 States by Average PM2.5 in 2023', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2023]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nsmallest(14, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Bottom 14 States by Average PM2.5 in 2023', width=500, height=300)
     return chart


</code>
"
1272,temporal_aggregation,Show the monthly average PM10 trend for Salem from 2017 to 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Salem'].copy()
    df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Salem (2017–2022)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Salem'].copy()
     df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Salem (2017–2022)', width=600, height=300)
     return chart


</code>
"
1273,temporal_aggregation,Show a monthly bar chart of the number of days Jammu and Kashmir exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2024.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Jammu and Kashmir') & (data['Timestamp'].dt.year == 2024)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Jammu and Kashmir Exceeded WHO PM2.5 Guideline per Month – 2024', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Jammu and Kashmir') & (data['Timestamp'].dt.year == 2024)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Jammu and Kashmir Exceeded WHO PM2.5 Guideline per Month – 2024', width=500, height=300)
     return chart


</code>
"
1274,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Uttar Pradesh, Nagaland, and Himachal Pradesh from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Uttar Pradesh', 'Nagaland', 'Himachal Pradesh']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Uttar Pradesh vs Nagaland vs Himachal Pradesh', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Uttar Pradesh', 'Nagaland', 'Himachal Pradesh']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Uttar Pradesh vs Nagaland vs Himachal Pradesh', width=550, height=320)
     return chart


</code>
"
1275,temporal_aggregation,Show a monthly bar chart of the number of days Tamil Nadu exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2024.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Tamil Nadu') & (data['Timestamp'].dt.year == 2024)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Tamil Nadu Exceeded WHO PM2.5 Guideline per Month – 2024', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Tamil Nadu') & (data['Timestamp'].dt.year == 2024)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Tamil Nadu Exceeded WHO PM2.5 Guideline per Month – 2024', width=500, height=300)
     return chart


</code>
"
1276,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Assam, Tripura, and Kerala from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Assam', 'Tripura', 'Kerala']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Assam vs Tripura vs Kerala', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Assam', 'Tripura', 'Kerala']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Assam vs Tripura vs Kerala', width=550, height=320)
     return chart


</code>
"
1277,temporal_aggregation,Plot the weekly average PM2.5 for Satna in 2021 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Satna') & (data['Timestamp'].dt.year == 2021)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Satna 2021', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Satna') & (data['Timestamp'].dt.year == 2021)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Satna 2021', width=600, height=300)
     return chart


</code>
"
1278,spatial_aggregation,Plot the top 11 states by average PM2.5 in 2023 as a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2023]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nlargest(11, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 11 States by Average PM2.5 in 2023', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2023]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nlargest(11, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 11 States by Average PM2.5 in 2023', width=500, height=300)
     return chart


</code>
"
1279,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Kerala, Jammu and Kashmir, and Mizoram in 2018 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Kerala', 'Jammu and Kashmir', 'Mizoram']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2018)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Kerala, Jammu and Kashmir, UP – 2018', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Kerala', 'Jammu and Kashmir', 'Mizoram']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2018)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Kerala, Jammu and Kashmir, UP – 2018', width=550, height=320)
     return chart


</code>
"
1280,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Uttar Pradesh, Mizoram, and Uttarakhand from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Uttar Pradesh', 'Mizoram', 'Uttarakhand']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Uttar Pradesh vs Mizoram vs Uttarakhand', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Uttar Pradesh', 'Mizoram', 'Uttarakhand']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Uttar Pradesh vs Mizoram vs Uttarakhand', width=550, height=320)
     return chart


</code>
"
1281,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Arunachal Pradesh stations in 2017, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Arunachal Pradesh') & (data['Timestamp'].dt.year == 2017)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Arunachal Pradesh Stations 2017', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Arunachal Pradesh') & (data['Timestamp'].dt.year == 2017)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Arunachal Pradesh Stations 2017', width=450, height=350)


</code>
"
1282,spatial_aggregation,Visualize the bottom 13 states with the lowest average PM2.5 in 2020 using a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2020]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nsmallest(13, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Bottom 13 States by Average PM2.5 in 2020', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2020]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nsmallest(13, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Bottom 13 States by Average PM2.5 in 2020', width=500, height=300)
     return chart


</code>
"
1283,specific_pattern,Show a cumulative area chart of PM2.5 readings for Bihar Sharif across 2024.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Bihar Sharif') & (data['Timestamp'].dt.year == 2024)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Bihar Sharif 2024', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Bihar Sharif') & (data['Timestamp'].dt.year == 2024)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Bihar Sharif 2024', width=600, height=300)
     return chart


</code>
"
1284,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Sikkim, Meghalaya, and Andhra Pradesh in 2017 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Sikkim', 'Meghalaya', 'Andhra Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2017)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Sikkim, Meghalaya, UP – 2017', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Sikkim', 'Meghalaya', 'Andhra Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2017)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Sikkim, Meghalaya, UP – 2017', width=550, height=320)
     return chart


</code>
"
1285,specific_pattern,Show a cumulative area chart of PM2.5 readings for Panipat across 2023.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Panipat') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Panipat 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Panipat') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Panipat 2023', width=600, height=300)
     return chart


</code>
"
1286,temporal_aggregation,Show the monthly average PM10 trend for Faridabad from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Faridabad'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Faridabad (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Faridabad'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Faridabad (2019–2024)', width=600, height=300)
     return chart


</code>
"
1287,spatial_aggregation,Visualize the bottom 11 states with the lowest average PM2.5 in 2019 using a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2019]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nsmallest(11, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Bottom 11 States by Average PM2.5 in 2019', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2019]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nsmallest(11, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Bottom 11 States by Average PM2.5 in 2019', width=500, height=300)
     return chart


</code>
"
1288,specific_pattern,Plot the rolling 30-day average PM2.5 for Nagaland in 2017 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Nagaland') & (data['Timestamp'].dt.year == 2017)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Nagaland 2017', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Nagaland') & (data['Timestamp'].dt.year == 2017)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Nagaland 2017', width=600, height=300)


</code>
"
1289,specific_pattern,Plot the rolling 30-day average PM2.5 for Uttar Pradesh in 2019 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Uttar Pradesh') & (data['Timestamp'].dt.year == 2019)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Uttar Pradesh 2019', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Uttar Pradesh') & (data['Timestamp'].dt.year == 2019)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Uttar Pradesh 2019', width=600, height=300)


</code>
"
1290,spatial_aggregation,Visualize the bottom 10 states with the lowest average PM2.5 in 2019 using a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2019]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nsmallest(10, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Bottom 10 States by Average PM2.5 in 2019', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2019]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nsmallest(10, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Bottom 10 States by Average PM2.5 in 2019', width=500, height=300)
     return chart


</code>
"
1291,temporal_aggregation,Show the monthly average PM10 trend for Jaipur from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Jaipur'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Jaipur (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Jaipur'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Jaipur (2019–2024)', width=600, height=300)
     return chart


</code>
"
1292,specific_pattern,Plot the rolling 30-day average PM2.5 for Haryana in 2020 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Haryana') & (data['Timestamp'].dt.year == 2020)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Haryana 2020', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Haryana') & (data['Timestamp'].dt.year == 2020)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Haryana 2020', width=600, height=300)


</code>
"
1293,spatial_aggregation,Show a bar chart of the top 12 cities by median PM2.5 in 2018.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2018]
    df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
    df = df.nlargest(12, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
        y=alt.Y('city:N', sort='-x', title='City'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
        tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 12 Cities by Median PM2.5 in 2018', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2018]
     df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
     df = df.nlargest(12, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
         y=alt.Y('city:N', sort='-x', title='City'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
         tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 12 Cities by Median PM2.5 in 2018', width=500, height=300)
     return chart


</code>
"
1294,specific_pattern,Show a cumulative area chart of PM2.5 readings for Solapur across 2018.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Solapur') & (data['Timestamp'].dt.year == 2018)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Solapur 2018', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Solapur') & (data['Timestamp'].dt.year == 2018)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Solapur 2018', width=600, height=300)
     return chart


</code>
"
1295,temporal_aggregation,Plot the weekly average PM2.5 for Amritsar in 2021 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Amritsar') & (data['Timestamp'].dt.year == 2021)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Amritsar 2021', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Amritsar') & (data['Timestamp'].dt.year == 2021)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Amritsar 2021', width=600, height=300)
     return chart


</code>
"
1296,specific_pattern,Plot the rolling 30-day average PM2.5 for Uttarakhand in 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Uttarakhand') & (data['Timestamp'].dt.year == 2022)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Uttarakhand 2022', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Uttarakhand') & (data['Timestamp'].dt.year == 2022)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Uttarakhand 2022', width=600, height=300)


</code>
"
1297,temporal_aggregation,Show the monthly average PM10 trend for Mangalore from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Mangalore'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Mangalore (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Mangalore'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Mangalore (2019–2024)', width=600, height=300)
     return chart


</code>
"
1298,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Rajasthan stations in 2020, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Rajasthan') & (data['Timestamp'].dt.year == 2020)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Rajasthan Stations 2020', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Rajasthan') & (data['Timestamp'].dt.year == 2020)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Rajasthan Stations 2020', width=450, height=350)


</code>
"
1299,spatial_aggregation,Show a bar chart of the top 8 cities by median PM2.5 in 2017.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2017]
    df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
    df = df.nlargest(8, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
        y=alt.Y('city:N', sort='-x', title='City'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
        tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 8 Cities by Median PM2.5 in 2017', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2017]
     df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
     df = df.nlargest(8, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
         y=alt.Y('city:N', sort='-x', title='City'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
         tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 8 Cities by Median PM2.5 in 2017', width=500, height=300)
     return chart


</code>
"
1300,specific_pattern,Show a cumulative area chart of PM2.5 readings for Vatva across 2022.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Vatva') & (data['Timestamp'].dt.year == 2022)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Vatva 2022', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Vatva') & (data['Timestamp'].dt.year == 2022)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Vatva 2022', width=600, height=300)
     return chart


</code>
"
1301,spatio_temporal_aggregation,"Create a faceted bar chart showing top 9 states by average PM2.5 per year for 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year.isin([2019,2020,2021,2022])].copy()
    df['Year'] = df['Timestamp'].dt.year
    agg = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    top = agg.groupby('Year').apply(lambda x: x.nlargest(9,'PM2.5')).reset_index(drop=True)
    chart = alt.Chart(top).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Avg PM2.5'),
        y=alt.Y('state:N', sort='-x'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).facet(facet='Year:O', columns=2).properties(title='Top 9 States by PM2.5 per Year')
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year.isin([2019,2020,2021,2022])].copy()
     df['Year'] = df['Timestamp'].dt.year
     agg = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     top = agg.groupby('Year').apply(lambda x: x.nlargest(9,'PM2.5')).reset_index(drop=True)
     chart = alt.Chart(top).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Avg PM2.5'),
         y=alt.Y('state:N', sort='-x'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).facet(facet='Year:O', columns=2).properties(title='Top 9 States by PM2.5 per Year')
     return chart


</code>
"
1302,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Himachal Pradesh, Himachal Pradesh, and Jharkhand from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Himachal Pradesh', 'Himachal Pradesh', 'Jharkhand']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Himachal Pradesh vs Himachal Pradesh vs Jharkhand', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Himachal Pradesh', 'Himachal Pradesh', 'Jharkhand']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Himachal Pradesh vs Himachal Pradesh vs Jharkhand', width=550, height=320)
     return chart


</code>
"
1303,temporal_aggregation,Show a monthly bar chart of the number of days Uttarakhand exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2022.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Uttarakhand') & (data['Timestamp'].dt.year == 2022)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Uttarakhand Exceeded WHO PM2.5 Guideline per Month – 2022', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Uttarakhand') & (data['Timestamp'].dt.year == 2022)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Uttarakhand Exceeded WHO PM2.5 Guideline per Month – 2022', width=500, height=300)
     return chart


</code>
"
1304,temporal_aggregation,Show a monthly bar chart of the number of days Meghalaya exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2023.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Meghalaya') & (data['Timestamp'].dt.year == 2023)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Meghalaya Exceeded WHO PM2.5 Guideline per Month – 2023', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Meghalaya') & (data['Timestamp'].dt.year == 2023)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Meghalaya Exceeded WHO PM2.5 Guideline per Month – 2023', width=500, height=300)
     return chart


</code>
"
1305,spatio_temporal_aggregation,Show the monthly average PM2.5 for Chhattisgarh across each year from 2017 to 2024 as small multiples (faceted by year).,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Chhattisgarh'].copy()
    df['Year'] = df['Timestamp'].dt.year
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='steelblue').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5'),
        tooltip=['Year:O','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).facet(
        facet=alt.Facet('Year:O', title='Year'),
        columns=4
    ).properties(title='Monthly PM2.5 in Chhattisgarh by Year (2017–2024)')
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Chhattisgarh'].copy()
     df['Year'] = df['Timestamp'].dt.year
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='steelblue').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5'),
         tooltip=['Year:O','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).facet(
         facet=alt.Facet('Year:O', title='Year'),
         columns=4
     ).properties(title='Monthly PM2.5 in Chhattisgarh by Year (2017–2024)')
     return chart


</code>
"
1306,specific_pattern,Show a cumulative area chart of PM2.5 readings for Darbhanga across 2023.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Darbhanga') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Darbhanga 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Darbhanga') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Darbhanga 2023', width=600, height=300)
     return chart


</code>
"
1307,temporal_aggregation,Show a monthly bar chart of the number of days Bihar exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2018.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Bihar') & (data['Timestamp'].dt.year == 2018)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Bihar Exceeded WHO PM2.5 Guideline per Month – 2018', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Bihar') & (data['Timestamp'].dt.year == 2018)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Bihar Exceeded WHO PM2.5 Guideline per Month – 2018', width=500, height=300)
     return chart


</code>
"
1308,spatial_aggregation,Visualize the bottom 14 states with the lowest average PM2.5 in 2019 using a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2019]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nsmallest(14, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Bottom 14 States by Average PM2.5 in 2019', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2019]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nsmallest(14, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Bottom 14 States by Average PM2.5 in 2019', width=500, height=300)
     return chart


</code>
"
1309,temporal_aggregation,Show the monthly average PM10 trend for Jorapokhar from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Jorapokhar'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Jorapokhar (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Jorapokhar'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Jorapokhar (2019–2024)', width=600, height=300)
     return chart


</code>
"
1310,spatio_temporal_aggregation,Show a heatmap of average PM2.5 for the top 8 most polluted states by month for 2018.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    top8 = data.groupby('state')['PM2.5'].mean().nlargest(8).index.tolist()
    df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2018)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_rect().encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('state:N', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
        tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Heatmap – Top 8 Polluted States by Month (2018)', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     top8 = data.groupby('state')['PM2.5'].mean().nlargest(8).index.tolist()
     df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2018)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_rect().encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('state:N', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
         tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Heatmap – Top 8 Polluted States by Month (2018)', width=500, height=300)
     return chart


</code>
"
1311,temporal_aggregation,Show a monthly bar chart of the number of days Himachal Pradesh exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2022.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Himachal Pradesh') & (data['Timestamp'].dt.year == 2022)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Himachal Pradesh Exceeded WHO PM2.5 Guideline per Month – 2022', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Himachal Pradesh') & (data['Timestamp'].dt.year == 2022)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Himachal Pradesh Exceeded WHO PM2.5 Guideline per Month – 2022', width=500, height=300)
     return chart


</code>
"
1312,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Madhya Pradesh, Assam, and Jharkhand from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Madhya Pradesh', 'Assam', 'Jharkhand']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Madhya Pradesh vs Assam vs Jharkhand', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Madhya Pradesh', 'Assam', 'Jharkhand']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Madhya Pradesh vs Assam vs Jharkhand', width=550, height=320)
     return chart


</code>
"
1313,temporal_aggregation,Show the monthly average PM2.5 for Tirupati in 2022 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Tirupati') & (data['Timestamp'].dt.year == 2022)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Tirupati 2022', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Tirupati') & (data['Timestamp'].dt.year == 2022)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Tirupati 2022', width=450, height=280)


</code>
"
1314,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Tamil Nadu, Sikkim, and Uttarakhand from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Tamil Nadu', 'Sikkim', 'Uttarakhand']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Tamil Nadu vs Sikkim vs Uttarakhand', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Tamil Nadu', 'Sikkim', 'Uttarakhand']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Tamil Nadu vs Sikkim vs Uttarakhand', width=550, height=320)
     return chart


</code>
"
1315,temporal_aggregation,Show the monthly average PM2.5 for Kunjemura in 2020 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Kunjemura') & (data['Timestamp'].dt.year == 2020)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Kunjemura 2020', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Kunjemura') & (data['Timestamp'].dt.year == 2020)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Kunjemura 2020', width=450, height=280)


</code>
"
1316,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Jharkhand, Andhra Pradesh, and Jharkhand across 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Jharkhand', 'Andhra Pradesh', 'Jharkhand']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Jharkhand', 'Andhra Pradesh', 'Jharkhand']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
     return chart


</code>
"
1317,temporal_aggregation,Show a monthly bar chart of the number of days Rajasthan exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2024.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Rajasthan') & (data['Timestamp'].dt.year == 2024)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Rajasthan Exceeded WHO PM2.5 Guideline per Month – 2024', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Rajasthan') & (data['Timestamp'].dt.year == 2024)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Rajasthan Exceeded WHO PM2.5 Guideline per Month – 2024', width=500, height=300)
     return chart


</code>
"
1318,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Chandigarh, Mizoram, and Telangana across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Chandigarh', 'Mizoram', 'Telangana']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Chandigarh', 'Mizoram', 'Telangana']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
1319,spatial_aggregation,Show a bar chart of the top 11 cities by median PM2.5 in 2021.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2021]
    df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
    df = df.nlargest(11, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
        y=alt.Y('city:N', sort='-x', title='City'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
        tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 11 Cities by Median PM2.5 in 2021', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2021]
     df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
     df = df.nlargest(11, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
         y=alt.Y('city:N', sort='-x', title='City'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
         tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 11 Cities by Median PM2.5 in 2021', width=500, height=300)
     return chart


</code>
"
1320,temporal_aggregation,Show the monthly average PM10 trend for Fatehabad from 2017 to 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Fatehabad'].copy()
    df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Fatehabad (2017–2022)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Fatehabad'].copy()
     df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Fatehabad (2017–2022)', width=600, height=300)
     return chart


</code>
"
1321,spatio_temporal_aggregation,Create a grouped bar chart comparing the average PM2.5 in Winter vs Summer for the top 8 most polluted states.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    top6 = data.groupby('state')['PM2.5'].mean().nlargest(8).index.tolist()
    df = data[data['state'].isin(top6)].copy()
    df['Season'] = df['Timestamp'].dt.month.apply(
        lambda m: 'Winter' if m in [12,1,2] else ('Summer' if m in [3,4,5] else None))
    df = df.dropna(subset=['Season'])
    df = df.groupby(['state','Season'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('state:N', title='State'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('Season:N', title='Season'),
        xOffset='Season:N',
        tooltip=['state:N','Season:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Winter vs Summer PM2.5 – Top 8 Polluted States', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     top6 = data.groupby('state')['PM2.5'].mean().nlargest(8).index.tolist()
     df = data[data['state'].isin(top6)].copy()
     df['Season'] = df['Timestamp'].dt.month.apply(
         lambda m: 'Winter' if m in [12,1,2] else ('Summer' if m in [3,4,5] else None))
     df = df.dropna(subset=['Season'])
     df = df.groupby(['state','Season'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('state:N', title='State'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('Season:N', title='Season'),
         xOffset='Season:N',
         tooltip=['state:N','Season:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Winter vs Summer PM2.5 – Top 8 Polluted States', width=550, height=320)
     return chart


</code>
"
1322,spatial_aggregation,"Show the top 5 states by average PM10 in 2024 as a bar chart, sorted in descending order.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2024]
    df = df.groupby('state')['PM10'].mean().reset_index().dropna()
    df = df.nlargest(5, 'PM10')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Top 5 States by Average PM10 in 2024', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2024]
     df = df.groupby('state')['PM10'].mean().reset_index().dropna()
     df = df.nlargest(5, 'PM10')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Top 5 States by Average PM10 in 2024', width=500, height=300)
     return chart


</code>
"
1323,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Katni, Chandrapur, and Sasaram in 2019 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Katni', 'Chandrapur', 'Sasaram']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Katni vs Chandrapur vs Sasaram – 2019', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Katni', 'Chandrapur', 'Sasaram']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Katni vs Chandrapur vs Sasaram – 2019', width=550, height=320)
     return chart


</code>
"
1324,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Maharashtra stations in 2018, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Maharashtra') & (data['Timestamp'].dt.year == 2018)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Maharashtra Stations 2018', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Maharashtra') & (data['Timestamp'].dt.year == 2018)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Maharashtra Stations 2018', width=450, height=350)


</code>
"
1325,specific_pattern,Show a cumulative area chart of PM2.5 readings for Jalgaon across 2023.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Jalgaon') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Jalgaon 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Jalgaon') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Jalgaon 2023', width=600, height=300)
     return chart


</code>
"
1326,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Nagaur, Chamarajanagar, and Sawai Madhopur in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Nagaur', 'Chamarajanagar', 'Sawai Madhopur']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Nagaur vs Chamarajanagar vs Sawai Madhopur – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Nagaur', 'Chamarajanagar', 'Sawai Madhopur']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Nagaur vs Chamarajanagar vs Sawai Madhopur – 2023', width=550, height=320)
     return chart


</code>
"
1327,temporal_aggregation,Show a monthly bar chart of the number of days Haryana exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2017.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Haryana') & (data['Timestamp'].dt.year == 2017)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Haryana Exceeded WHO PM2.5 Guideline per Month – 2017', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Haryana') & (data['Timestamp'].dt.year == 2017)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Haryana Exceeded WHO PM2.5 Guideline per Month – 2017', width=500, height=300)
     return chart


</code>
"
1328,spatial_aggregation,Plot the distribution of PM2.5 values in Uttarakhand across all years using a histogram.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Uttarakhand'].dropna(subset=['PM2.5'])
    df = df[['PM2.5']]
    chart = alt.Chart(df).mark_bar(color='steelblue', opacity=0.75).encode(
        x=alt.X('PM2\.5:Q', bin=alt.Bin(maxbins=40), title='PM2.5 (µg/m³)'),
        y=alt.Y('count()', title='Number of Observations'),
        tooltip=[alt.Tooltip('PM2\.5:Q', bin=True), 'count()']
    ).properties(title='PM2.5 Distribution – Uttarakhand (All Years)', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Uttarakhand'].dropna(subset=['PM2.5'])
     df = df[['PM2.5']]
     chart = alt.Chart(df).mark_bar(color='steelblue', opacity=0.75).encode(
         x=alt.X('PM2\.5:Q', bin=alt.Bin(maxbins=40), title='PM2.5 (µg/m³)'),
         y=alt.Y('count()', title='Number of Observations'),
         tooltip=[alt.Tooltip('PM2\.5:Q', bin=True), 'count()']
     ).properties(title='PM2.5 Distribution – Uttarakhand (All Years)', width=500, height=300)
     return chart


</code>
"
1329,temporal_aggregation,Show a monthly bar chart of the number of days Jammu and Kashmir exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2022.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Jammu and Kashmir') & (data['Timestamp'].dt.year == 2022)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Jammu and Kashmir Exceeded WHO PM2.5 Guideline per Month – 2022', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Jammu and Kashmir') & (data['Timestamp'].dt.year == 2022)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Jammu and Kashmir Exceeded WHO PM2.5 Guideline per Month – 2022', width=500, height=300)
     return chart


</code>
"
1330,specific_pattern,Plot the rolling 30-day average PM2.5 for Himachal Pradesh in 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Himachal Pradesh') & (data['Timestamp'].dt.year == 2024)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Himachal Pradesh 2024', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Himachal Pradesh') & (data['Timestamp'].dt.year == 2024)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Himachal Pradesh 2024', width=600, height=300)


</code>
"
1331,specific_pattern,Show a cumulative area chart of PM2.5 readings for Munger across 2023.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Munger') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Munger 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Munger') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Munger 2023', width=600, height=300)
     return chart


</code>
"
1332,specific_pattern,Show a cumulative area chart of PM2.5 readings for Buxar across 2024.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Buxar') & (data['Timestamp'].dt.year == 2024)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Buxar 2024', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Buxar') & (data['Timestamp'].dt.year == 2024)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Buxar 2024', width=600, height=300)
     return chart


</code>
"
1333,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Uttarakhand stations in 2020, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Uttarakhand') & (data['Timestamp'].dt.year == 2020)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Uttarakhand Stations 2020', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Uttarakhand') & (data['Timestamp'].dt.year == 2020)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Uttarakhand Stations 2020', width=450, height=350)


</code>
"
1334,spatio_temporal_aggregation,"Create a faceted bar chart showing top 7 states by average PM2.5 per year for 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year.isin([2020,2021,2022,2023])].copy()
    df['Year'] = df['Timestamp'].dt.year
    agg = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    top = agg.groupby('Year').apply(lambda x: x.nlargest(7,'PM2.5')).reset_index(drop=True)
    chart = alt.Chart(top).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Avg PM2.5'),
        y=alt.Y('state:N', sort='-x'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).facet(facet='Year:O', columns=2).properties(title='Top 7 States by PM2.5 per Year')
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year.isin([2020,2021,2022,2023])].copy()
     df['Year'] = df['Timestamp'].dt.year
     agg = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     top = agg.groupby('Year').apply(lambda x: x.nlargest(7,'PM2.5')).reset_index(drop=True)
     chart = alt.Chart(top).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Avg PM2.5'),
         y=alt.Y('state:N', sort='-x'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).facet(facet='Year:O', columns=2).properties(title='Top 7 States by PM2.5 per Year')
     return chart


</code>
"
1335,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Chandigarh, Punjab, and Tripura from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Chandigarh', 'Punjab', 'Tripura']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Chandigarh vs Punjab vs Tripura', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Chandigarh', 'Punjab', 'Tripura']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Chandigarh vs Punjab vs Tripura', width=550, height=320)
     return chart


</code>
"
1336,specific_pattern,Show a cumulative area chart of PM2.5 readings for Bundi across 2024.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Bundi') & (data['Timestamp'].dt.year == 2024)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Bundi 2024', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Bundi') & (data['Timestamp'].dt.year == 2024)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Bundi 2024', width=600, height=300)
     return chart


</code>
"
1337,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Delhi, Arunachal Pradesh, and Bihar across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Delhi', 'Arunachal Pradesh', 'Bihar']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Delhi', 'Arunachal Pradesh', 'Bihar']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
1338,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Ajmer, Kolar, and Barrackpore in 2017 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Ajmer', 'Kolar', 'Barrackpore']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2017)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Ajmer vs Kolar vs Barrackpore – 2017', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Ajmer', 'Kolar', 'Barrackpore']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2017)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Ajmer vs Kolar vs Barrackpore – 2017', width=550, height=320)
     return chart


</code>
"
1339,spatial_aggregation,"Show the top 7 states by average PM10 in 2017 as a bar chart, sorted in descending order.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2017]
    df = df.groupby('state')['PM10'].mean().reset_index().dropna()
    df = df.nlargest(7, 'PM10')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Top 7 States by Average PM10 in 2017', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2017]
     df = df.groupby('state')['PM10'].mean().reset_index().dropna()
     df = df.nlargest(7, 'PM10')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Top 7 States by Average PM10 in 2017', width=500, height=300)
     return chart


</code>
"
1340,temporal_aggregation,Show the monthly average PM2.5 for Dholpur in 2019 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Dholpur') & (data['Timestamp'].dt.year == 2019)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Dholpur 2019', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Dholpur') & (data['Timestamp'].dt.year == 2019)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Dholpur 2019', width=450, height=280)


</code>
"
1341,temporal_aggregation,Plot the weekly average PM2.5 for Howrah in 2021 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Howrah') & (data['Timestamp'].dt.year == 2021)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Howrah 2021', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Howrah') & (data['Timestamp'].dt.year == 2021)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Howrah 2021', width=600, height=300)
     return chart


</code>
"
1342,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Sikkim stations in 2017, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Sikkim') & (data['Timestamp'].dt.year == 2017)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Sikkim Stations 2017', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Sikkim') & (data['Timestamp'].dt.year == 2017)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Sikkim Stations 2017', width=450, height=350)


</code>
"
1343,spatio_temporal_aggregation,Show a heatmap of average PM2.5 for the top 9 most polluted states by month for 2023.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    top8 = data.groupby('state')['PM2.5'].mean().nlargest(9).index.tolist()
    df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_rect().encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('state:N', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
        tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Heatmap – Top 9 Polluted States by Month (2023)', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     top8 = data.groupby('state')['PM2.5'].mean().nlargest(9).index.tolist()
     df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_rect().encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('state:N', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
         tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Heatmap – Top 9 Polluted States by Month (2023)', width=500, height=300)
     return chart


</code>
"
1344,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Tripura, Uttar Pradesh, and Assam across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Tripura', 'Uttar Pradesh', 'Assam']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Tripura', 'Uttar Pradesh', 'Assam']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
1345,specific_pattern,Plot the rolling 30-day average PM2.5 for Karnataka in 2017 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Karnataka') & (data['Timestamp'].dt.year == 2017)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Karnataka 2017', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Karnataka') & (data['Timestamp'].dt.year == 2017)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Karnataka 2017', width=600, height=300)


</code>
"
1346,specific_pattern,Show a cumulative area chart of PM2.5 readings for Bilaspur across 2022.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Bilaspur') & (data['Timestamp'].dt.year == 2022)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Bilaspur 2022', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Bilaspur') & (data['Timestamp'].dt.year == 2022)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Bilaspur 2022', width=600, height=300)
     return chart


</code>
"
1347,temporal_aggregation,Show the monthly average PM2.5 for Gorakhpur in 2019 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Gorakhpur') & (data['Timestamp'].dt.year == 2019)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Gorakhpur 2019', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Gorakhpur') & (data['Timestamp'].dt.year == 2019)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Gorakhpur 2019', width=450, height=280)


</code>
"
1348,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Telangana, Rajasthan, and Kerala from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Telangana', 'Rajasthan', 'Kerala']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Telangana vs Rajasthan vs Kerala', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Telangana', 'Rajasthan', 'Kerala']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Telangana vs Rajasthan vs Kerala', width=550, height=320)
     return chart


</code>
"
1349,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Odisha, Chandigarh, and West Bengal from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Odisha', 'Chandigarh', 'West Bengal']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Odisha vs Chandigarh vs West Bengal', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Odisha', 'Chandigarh', 'West Bengal']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Odisha vs Chandigarh vs West Bengal', width=550, height=320)
     return chart


</code>
"
1350,spatial_aggregation,Show a bar chart of the top 15 cities by median PM2.5 in 2020.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2020]
    df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
    df = df.nlargest(15, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
        y=alt.Y('city:N', sort='-x', title='City'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
        tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 15 Cities by Median PM2.5 in 2020', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2020]
     df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
     df = df.nlargest(15, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
         y=alt.Y('city:N', sort='-x', title='City'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
         tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 15 Cities by Median PM2.5 in 2020', width=500, height=300)
     return chart


</code>
"
1351,specific_pattern,Show PM2.5 exceedances above the Indian standard (60 µg/m³) per year as a bar chart for Sikkim.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Sikkim'].dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 60].copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby('Year')['Timestamp'].nunique().reset_index()
    df.columns = ['Year','Days Exceeded']
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('Days Exceeded:Q', title='Days PM2.5 > 60 µg/m³'),
        tooltip=['Year:N','Days Exceeded:Q']
    ).properties(title='Days Sikkim Exceeded Indian PM2.5 Standard (60 µg/m³) per Year', width=450, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Sikkim'].dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 60].copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby('Year')['Timestamp'].nunique().reset_index()
     df.columns = ['Year','Days Exceeded']
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('Days Exceeded:Q', title='Days PM2.5 > 60 µg/m³'),
         tooltip=['Year:N','Days Exceeded:Q']
     ).properties(title='Days Sikkim Exceeded Indian PM2.5 Standard (60 µg/m³) per Year', width=450, height=300)
     return chart


</code>
"
1352,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Madhya Pradesh, Jammu and Kashmir, and Madhya Pradesh across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Madhya Pradesh', 'Jammu and Kashmir', 'Madhya Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Madhya Pradesh', 'Jammu and Kashmir', 'Madhya Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
1353,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Himachal Pradesh, Karnataka, and Punjab across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Himachal Pradesh', 'Karnataka', 'Punjab']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Himachal Pradesh', 'Karnataka', 'Punjab']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
1354,specific_pattern,Plot the rolling 30-day average PM2.5 for Puducherry in 2020 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Puducherry') & (data['Timestamp'].dt.year == 2020)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Puducherry 2020', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Puducherry') & (data['Timestamp'].dt.year == 2020)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Puducherry 2020', width=600, height=300)


</code>
"
1355,temporal_aggregation,Show a monthly bar chart of the number of days Arunachal Pradesh exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2022.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Arunachal Pradesh') & (data['Timestamp'].dt.year == 2022)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Arunachal Pradesh Exceeded WHO PM2.5 Guideline per Month – 2022', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Arunachal Pradesh') & (data['Timestamp'].dt.year == 2022)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Arunachal Pradesh Exceeded WHO PM2.5 Guideline per Month – 2022', width=500, height=300)
     return chart


</code>
"
1356,specific_pattern,Plot the rolling 30-day average PM2.5 for Sikkim in 2019 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Sikkim') & (data['Timestamp'].dt.year == 2019)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Sikkim 2019', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Sikkim') & (data['Timestamp'].dt.year == 2019)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Sikkim 2019', width=600, height=300)


</code>
"
1357,temporal_aggregation,Show the monthly average PM10 trend for Manguraha from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Manguraha'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Manguraha (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Manguraha'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Manguraha (2019–2024)', width=600, height=300)
     return chart


</code>
"
1358,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Jharkhand, Arunachal Pradesh, and Kerala across 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Jharkhand', 'Arunachal Pradesh', 'Kerala']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Jharkhand', 'Arunachal Pradesh', 'Kerala']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
     return chart


</code>
"
1359,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Himachal Pradesh, Odisha, and Mizoram in 2018 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Himachal Pradesh', 'Odisha', 'Mizoram']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2018)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Himachal Pradesh, Odisha, UP – 2018', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Himachal Pradesh', 'Odisha', 'Mizoram']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2018)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Himachal Pradesh, Odisha, UP – 2018', width=550, height=320)
     return chart


</code>
"
1360,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Haryana, Punjab, and Andhra Pradesh across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Haryana', 'Punjab', 'Andhra Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Haryana', 'Punjab', 'Andhra Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
1361,specific_pattern,Show PM2.5 exceedances above the Indian standard (60 µg/m³) per year as a bar chart for Jammu and Kashmir.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Jammu and Kashmir'].dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 60].copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby('Year')['Timestamp'].nunique().reset_index()
    df.columns = ['Year','Days Exceeded']
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('Days Exceeded:Q', title='Days PM2.5 > 60 µg/m³'),
        tooltip=['Year:N','Days Exceeded:Q']
    ).properties(title='Days Jammu and Kashmir Exceeded Indian PM2.5 Standard (60 µg/m³) per Year', width=450, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Jammu and Kashmir'].dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 60].copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby('Year')['Timestamp'].nunique().reset_index()
     df.columns = ['Year','Days Exceeded']
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('Days Exceeded:Q', title='Days PM2.5 > 60 µg/m³'),
         tooltip=['Year:N','Days Exceeded:Q']
     ).properties(title='Days Jammu and Kashmir Exceeded Indian PM2.5 Standard (60 µg/m³) per Year', width=450, height=300)
     return chart


</code>
"
1362,temporal_aggregation,Plot the weekly average PM2.5 for Kaithal in 2019 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Kaithal') & (data['Timestamp'].dt.year == 2019)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Kaithal 2019', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Kaithal') & (data['Timestamp'].dt.year == 2019)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Kaithal 2019', width=600, height=300)
     return chart


</code>
"
1363,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Nagaland, West Bengal, and Rajasthan from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Nagaland', 'West Bengal', 'Rajasthan']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Nagaland vs West Bengal vs Rajasthan', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Nagaland', 'West Bengal', 'Rajasthan']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Nagaland vs West Bengal vs Rajasthan', width=550, height=320)
     return chart


</code>
"
1364,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Jharkhand, Assam, and Tamil Nadu across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Jharkhand', 'Assam', 'Tamil Nadu']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Jharkhand', 'Assam', 'Tamil Nadu']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
1365,specific_pattern,Show PM2.5 exceedances above the Indian standard (60 µg/m³) per year as a bar chart for Meghalaya.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Meghalaya'].dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 60].copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby('Year')['Timestamp'].nunique().reset_index()
    df.columns = ['Year','Days Exceeded']
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('Days Exceeded:Q', title='Days PM2.5 > 60 µg/m³'),
        tooltip=['Year:N','Days Exceeded:Q']
    ).properties(title='Days Meghalaya Exceeded Indian PM2.5 Standard (60 µg/m³) per Year', width=450, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Meghalaya'].dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 60].copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby('Year')['Timestamp'].nunique().reset_index()
     df.columns = ['Year','Days Exceeded']
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('Days Exceeded:Q', title='Days PM2.5 > 60 µg/m³'),
         tooltip=['Year:N','Days Exceeded:Q']
     ).properties(title='Days Meghalaya Exceeded Indian PM2.5 Standard (60 µg/m³) per Year', width=450, height=300)
     return chart


</code>
"
1366,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Haryana stations in 2020, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Haryana') & (data['Timestamp'].dt.year == 2020)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Haryana Stations 2020', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Haryana') & (data['Timestamp'].dt.year == 2020)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Haryana Stations 2020', width=450, height=350)


</code>
"
1367,temporal_aggregation,Plot the weekly average PM2.5 for Samastipur in 2023 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Samastipur') & (data['Timestamp'].dt.year == 2023)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Samastipur 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Samastipur') & (data['Timestamp'].dt.year == 2023)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Samastipur 2023', width=600, height=300)
     return chart


</code>
"
1368,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Manipur, Jharkhand, and Delhi across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Manipur', 'Jharkhand', 'Delhi']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Manipur', 'Jharkhand', 'Delhi']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
1369,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Puducherry, Rajasthan, and Sikkim in 2018 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Puducherry', 'Rajasthan', 'Sikkim']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2018)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Puducherry, Rajasthan, UP – 2018', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Puducherry', 'Rajasthan', 'Sikkim']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2018)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Puducherry, Rajasthan, UP – 2018', width=550, height=320)
     return chart


</code>
"
1370,spatial_aggregation,Show a bar chart of the top 8 cities by median PM2.5 in 2023.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2023]
    df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
    df = df.nlargest(8, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
        y=alt.Y('city:N', sort='-x', title='City'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
        tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 8 Cities by Median PM2.5 in 2023', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2023]
     df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
     df = df.nlargest(8, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
         y=alt.Y('city:N', sort='-x', title='City'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
         tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 8 Cities by Median PM2.5 in 2023', width=500, height=300)
     return chart


</code>
"
1371,temporal_aggregation,Show the monthly average PM2.5 for Jalgaon in 2017 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Jalgaon') & (data['Timestamp'].dt.year == 2017)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Jalgaon 2017', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Jalgaon') & (data['Timestamp'].dt.year == 2017)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Jalgaon 2017', width=450, height=280)


</code>
"
1372,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Delhi, Haryana, and Chhattisgarh across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Delhi', 'Haryana', 'Chhattisgarh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Delhi', 'Haryana', 'Chhattisgarh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
1373,temporal_aggregation,Show the monthly average PM10 trend for Jodhpur from 2017 to 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Jodhpur'].copy()
    df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Jodhpur (2017–2022)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Jodhpur'].copy()
     df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Jodhpur (2017–2022)', width=600, height=300)
     return chart


</code>
"
1374,spatio_temporal_aggregation,Create a heatmap showing the average PM2.5 by month (x-axis) and year (y-axis) for Nagaland.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Nagaland'].copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_rect().encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Year:N', title='Year'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='Avg PM2.5'),
        tooltip=['Year:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Heatmap – Nagaland (Month × Year)', width=500, height=280)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Nagaland'].copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_rect().encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Year:N', title='Year'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='Avg PM2.5'),
         tooltip=['Year:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Heatmap – Nagaland (Month × Year)', width=500, height=280)
     return chart


</code>
"
1375,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Sikkim, Bihar, and Tamil Nadu in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Sikkim', 'Bihar', 'Tamil Nadu']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Sikkim, Bihar, UP – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Sikkim', 'Bihar', 'Tamil Nadu']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Sikkim, Bihar, UP – 2022', width=550, height=320)
     return chart


</code>
"
1376,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Chandigarh, Delhi, and Bihar in 2021 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Chandigarh', 'Delhi', 'Bihar']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Chandigarh, Delhi, UP – 2021', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Chandigarh', 'Delhi', 'Bihar']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Chandigarh, Delhi, UP – 2021', width=550, height=320)
     return chart


</code>
"
1377,temporal_aggregation,Plot the weekly average PM2.5 for Rajamahendravaram in 2019 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Rajamahendravaram') & (data['Timestamp'].dt.year == 2019)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Rajamahendravaram 2019', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Rajamahendravaram') & (data['Timestamp'].dt.year == 2019)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Rajamahendravaram 2019', width=600, height=300)
     return chart


</code>
"
1378,spatio_temporal_aggregation,Show a heatmap of average PM2.5 for the top 6 most polluted states by month for 2023.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    top8 = data.groupby('state')['PM2.5'].mean().nlargest(6).index.tolist()
    df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_rect().encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('state:N', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
        tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Heatmap – Top 6 Polluted States by Month (2023)', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     top8 = data.groupby('state')['PM2.5'].mean().nlargest(6).index.tolist()
     df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_rect().encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('state:N', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
         tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Heatmap – Top 6 Polluted States by Month (2023)', width=500, height=300)
     return chart


</code>
"
1379,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Puducherry, West Bengal, and Gujarat across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Puducherry', 'West Bengal', 'Gujarat']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Puducherry', 'West Bengal', 'Gujarat']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
1380,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Munger, Koppal, and Nandesari in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Munger', 'Koppal', 'Nandesari']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Munger vs Koppal vs Nandesari – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Munger', 'Koppal', 'Nandesari']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Munger vs Koppal vs Nandesari – 2022', width=550, height=320)
     return chart


</code>
"
1381,specific_pattern,Plot the rolling 30-day average PM2.5 for Meghalaya in 2017 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Meghalaya') & (data['Timestamp'].dt.year == 2017)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Meghalaya 2017', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Meghalaya') & (data['Timestamp'].dt.year == 2017)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Meghalaya 2017', width=600, height=300)


</code>
"
1382,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Karnataka, Madhya Pradesh, and Andhra Pradesh in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Karnataka', 'Madhya Pradesh', 'Andhra Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Karnataka, Madhya Pradesh, UP – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Karnataka', 'Madhya Pradesh', 'Andhra Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Karnataka, Madhya Pradesh, UP – 2024', width=550, height=320)
     return chart


</code>
"
1383,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Arunachal Pradesh stations in 2024, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Arunachal Pradesh') & (data['Timestamp'].dt.year == 2024)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Arunachal Pradesh Stations 2024', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Arunachal Pradesh') & (data['Timestamp'].dt.year == 2024)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Arunachal Pradesh Stations 2024', width=450, height=350)


</code>
"
1384,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Jammu and Kashmir stations in 2024, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Jammu and Kashmir') & (data['Timestamp'].dt.year == 2024)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Jammu and Kashmir Stations 2024', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Jammu and Kashmir') & (data['Timestamp'].dt.year == 2024)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Jammu and Kashmir Stations 2024', width=450, height=350)


</code>
"
1385,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Telangana, Punjab, and Chhattisgarh across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Telangana', 'Punjab', 'Chhattisgarh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Telangana', 'Punjab', 'Chhattisgarh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
1386,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Pithampur, Milupara, and Barrackpore in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Pithampur', 'Milupara', 'Barrackpore']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Pithampur vs Milupara vs Barrackpore – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Pithampur', 'Milupara', 'Barrackpore']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Pithampur vs Milupara vs Barrackpore – 2022', width=550, height=320)
     return chart


</code>
"
1387,temporal_aggregation,Show the monthly average PM10 trend for Palwal from 2017 to 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Palwal'].copy()
    df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Palwal (2017–2022)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Palwal'].copy()
     df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Palwal (2017–2022)', width=600, height=300)
     return chart


</code>
"
1388,temporal_aggregation,Show the monthly average PM2.5 for Kolkata in 2023 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Kolkata') & (data['Timestamp'].dt.year == 2023)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Kolkata 2023', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Kolkata') & (data['Timestamp'].dt.year == 2023)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Kolkata 2023', width=450, height=280)


</code>
"
1389,temporal_aggregation,Show the monthly average PM10 trend for Dhule from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Dhule'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Dhule (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Dhule'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Dhule (2019–2024)', width=600, height=300)
     return chart


</code>
"
1390,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Uttarakhand, Madhya Pradesh, and Assam from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Uttarakhand', 'Madhya Pradesh', 'Assam']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Uttarakhand vs Madhya Pradesh vs Assam', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Uttarakhand', 'Madhya Pradesh', 'Assam']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Uttarakhand vs Madhya Pradesh vs Assam', width=550, height=320)
     return chart


</code>
"
1391,temporal_aggregation,Show the monthly average PM2.5 for Dharwad in 2019 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Dharwad') & (data['Timestamp'].dt.year == 2019)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Dharwad 2019', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Dharwad') & (data['Timestamp'].dt.year == 2019)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Dharwad 2019', width=450, height=280)


</code>
"
1392,temporal_aggregation,Show a monthly bar chart of the number of days Bihar exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2022.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Bihar') & (data['Timestamp'].dt.year == 2022)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Bihar Exceeded WHO PM2.5 Guideline per Month – 2022', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Bihar') & (data['Timestamp'].dt.year == 2022)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Bihar Exceeded WHO PM2.5 Guideline per Month – 2022', width=500, height=300)
     return chart


</code>
"
1393,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Telangana, Maharashtra, and Puducherry across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Telangana', 'Maharashtra', 'Puducherry']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Telangana', 'Maharashtra', 'Puducherry']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
1394,temporal_aggregation,Plot the weekly average PM2.5 for Kalaburagi in 2018 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Kalaburagi') & (data['Timestamp'].dt.year == 2018)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Kalaburagi 2018', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Kalaburagi') & (data['Timestamp'].dt.year == 2018)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Kalaburagi 2018', width=600, height=300)
     return chart


</code>
"
1395,temporal_aggregation,Show the monthly average PM10 trend for Gurugram from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Gurugram'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Gurugram (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Gurugram'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Gurugram (2019–2024)', width=600, height=300)
     return chart


</code>
"
1396,specific_pattern,Plot the rolling 30-day average PM2.5 for Kerala in 2018 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Kerala') & (data['Timestamp'].dt.year == 2018)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Kerala 2018', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Kerala') & (data['Timestamp'].dt.year == 2018)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Kerala 2018', width=600, height=300)


</code>
"
1397,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Chandigarh stations in 2024, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Chandigarh') & (data['Timestamp'].dt.year == 2024)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Chandigarh Stations 2024', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Chandigarh') & (data['Timestamp'].dt.year == 2024)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Chandigarh Stations 2024', width=450, height=350)


</code>
"
1398,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Uttarakhand, Madhya Pradesh, and Gujarat in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Uttarakhand', 'Madhya Pradesh', 'Gujarat']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Uttarakhand, Madhya Pradesh, UP – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Uttarakhand', 'Madhya Pradesh', 'Gujarat']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Uttarakhand, Madhya Pradesh, UP – 2022', width=550, height=320)
     return chart


</code>
"
1399,temporal_aggregation,Plot the weekly average PM2.5 for Nandesari in 2023 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Nandesari') & (data['Timestamp'].dt.year == 2023)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Nandesari 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Nandesari') & (data['Timestamp'].dt.year == 2023)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Nandesari 2023', width=600, height=300)
     return chart


</code>
"
1400,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Assam, Meghalaya, and Bihar across 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Assam', 'Meghalaya', 'Bihar']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Assam', 'Meghalaya', 'Bihar']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
     return chart


</code>
"
1401,spatio_temporal_aggregation,Show a heatmap of average PM2.5 for the top 13 most polluted states by month for 2018.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    top8 = data.groupby('state')['PM2.5'].mean().nlargest(13).index.tolist()
    df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2018)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_rect().encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('state:N', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
        tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Heatmap – Top 13 Polluted States by Month (2018)', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     top8 = data.groupby('state')['PM2.5'].mean().nlargest(13).index.tolist()
     df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2018)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_rect().encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('state:N', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
         tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Heatmap – Top 13 Polluted States by Month (2018)', width=500, height=300)
     return chart


</code>
"
1402,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Mandi Gobindgarh, Sivasagar, and Srinagar in 2018 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Mandi Gobindgarh', 'Sivasagar', 'Srinagar']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2018)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Mandi Gobindgarh vs Sivasagar vs Srinagar – 2018', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Mandi Gobindgarh', 'Sivasagar', 'Srinagar']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2018)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Mandi Gobindgarh vs Sivasagar vs Srinagar – 2018', width=550, height=320)
     return chart


</code>
"
1403,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Chengalpattu, Bagalkot, and Kota in 2017 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Chengalpattu', 'Bagalkot', 'Kota']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2017)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Chengalpattu vs Bagalkot vs Kota – 2017', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Chengalpattu', 'Bagalkot', 'Kota']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2017)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Chengalpattu vs Bagalkot vs Kota – 2017', width=550, height=320)
     return chart


</code>
"
1404,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Uttar Pradesh, Nagaland, and Haryana across 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Uttar Pradesh', 'Nagaland', 'Haryana']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Uttar Pradesh', 'Nagaland', 'Haryana']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
     return chart


</code>
"
1405,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Dhanbad, Raipur, and Kota in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Dhanbad', 'Raipur', 'Kota']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Dhanbad vs Raipur vs Kota – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Dhanbad', 'Raipur', 'Kota']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Dhanbad vs Raipur vs Kota – 2022', width=550, height=320)
     return chart


</code>
"
1406,temporal_aggregation,Show the monthly average PM2.5 for Banswara in 2018 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Banswara') & (data['Timestamp'].dt.year == 2018)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Banswara 2018', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Banswara') & (data['Timestamp'].dt.year == 2018)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Banswara 2018', width=450, height=280)


</code>
"
1407,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Chhattisgarh, Maharashtra, and Arunachal Pradesh from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Chhattisgarh', 'Maharashtra', 'Arunachal Pradesh']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Chhattisgarh vs Maharashtra vs Arunachal Pradesh', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Chhattisgarh', 'Maharashtra', 'Arunachal Pradesh']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Chhattisgarh vs Maharashtra vs Arunachal Pradesh', width=550, height=320)
     return chart


</code>
"
1408,temporal_aggregation,Plot the monthly average PM2.5 trend for Jharkhand from 2017 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Jharkhand'].copy()
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='steelblue').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly Average PM2.5 Trend – Jharkhand (2017–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Jharkhand'].copy()
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='steelblue').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly Average PM2.5 Trend – Jharkhand (2017–2024)', width=600, height=300)
     return chart


</code>
"
1409,temporal_aggregation,Show a monthly bar chart of the number of days Manipur exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2023.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Manipur') & (data['Timestamp'].dt.year == 2023)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Manipur Exceeded WHO PM2.5 Guideline per Month – 2023', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Manipur') & (data['Timestamp'].dt.year == 2023)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Manipur Exceeded WHO PM2.5 Guideline per Month – 2023', width=500, height=300)
     return chart


</code>
"
1410,temporal_aggregation,Plot the weekly average PM2.5 for Ahmedabad in 2021 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Ahmedabad') & (data['Timestamp'].dt.year == 2021)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Ahmedabad 2021', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Ahmedabad') & (data['Timestamp'].dt.year == 2021)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Ahmedabad 2021', width=600, height=300)
     return chart


</code>
"
1411,spatial_aggregation,"Show the top 15 states by average PM10 in 2020 as a bar chart, sorted in descending order.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2020]
    df = df.groupby('state')['PM10'].mean().reset_index().dropna()
    df = df.nlargest(15, 'PM10')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Top 15 States by Average PM10 in 2020', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2020]
     df = df.groupby('state')['PM10'].mean().reset_index().dropna()
     df = df.nlargest(15, 'PM10')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Top 15 States by Average PM10 in 2020', width=500, height=300)
     return chart


</code>
"
1412,spatio_temporal_aggregation,Show a heatmap of average PM2.5 for the top 6 most polluted states by month for 2022.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    top8 = data.groupby('state')['PM2.5'].mean().nlargest(6).index.tolist()
    df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_rect().encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('state:N', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
        tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Heatmap – Top 6 Polluted States by Month (2022)', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     top8 = data.groupby('state')['PM2.5'].mean().nlargest(6).index.tolist()
     df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_rect().encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('state:N', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
         tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Heatmap – Top 6 Polluted States by Month (2022)', width=500, height=300)
     return chart


</code>
"
1413,temporal_aggregation,Plot the weekly average PM2.5 for Bahadurgarh in 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Bahadurgarh') & (data['Timestamp'].dt.year == 2024)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Bahadurgarh 2024', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Bahadurgarh') & (data['Timestamp'].dt.year == 2024)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Bahadurgarh 2024', width=600, height=300)
     return chart


</code>
"
1414,spatial_aggregation,Show a bar chart of the top 14 cities by median PM2.5 in 2021.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2021]
    df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
    df = df.nlargest(14, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
        y=alt.Y('city:N', sort='-x', title='City'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
        tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 14 Cities by Median PM2.5 in 2021', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2021]
     df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
     df = df.nlargest(14, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
         y=alt.Y('city:N', sort='-x', title='City'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
         tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 14 Cities by Median PM2.5 in 2021', width=500, height=300)
     return chart


</code>
"
1415,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Karnataka, Tripura, and Assam in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Karnataka', 'Tripura', 'Assam']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Karnataka, Tripura, UP – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Karnataka', 'Tripura', 'Assam']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Karnataka, Tripura, UP – 2024', width=550, height=320)
     return chart


</code>
"
1416,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Uttar Pradesh, Nagaland, and Puducherry across 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Uttar Pradesh', 'Nagaland', 'Puducherry']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Uttar Pradesh', 'Nagaland', 'Puducherry']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
     return chart


</code>
"
1417,temporal_aggregation,Show the monthly average PM10 trend for Bidar from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Bidar'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Bidar (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Bidar'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Bidar (2019–2024)', width=600, height=300)
     return chart


</code>
"
1418,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Nagaland, Kerala, and Chhattisgarh across 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Nagaland', 'Kerala', 'Chhattisgarh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Nagaland', 'Kerala', 'Chhattisgarh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
     return chart


</code>
"
1419,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Odisha, Punjab, and Puducherry from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Odisha', 'Punjab', 'Puducherry']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Odisha vs Punjab vs Puducherry', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Odisha', 'Punjab', 'Puducherry']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Odisha vs Punjab vs Puducherry', width=550, height=320)
     return chart


</code>
"
1420,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Uttarakhand, Maharashtra, and Tamil Nadu across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Uttarakhand', 'Maharashtra', 'Tamil Nadu']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Uttarakhand', 'Maharashtra', 'Tamil Nadu']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
1421,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Himachal Pradesh, Kerala, and Mizoram in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Himachal Pradesh', 'Kerala', 'Mizoram']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Himachal Pradesh, Kerala, UP – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Himachal Pradesh', 'Kerala', 'Mizoram']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Himachal Pradesh, Kerala, UP – 2022', width=550, height=320)
     return chart


</code>
"
1422,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Bihar, Puducherry, and Madhya Pradesh across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Bihar', 'Puducherry', 'Madhya Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Bihar', 'Puducherry', 'Madhya Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
1423,spatial_aggregation,"Show the top 15 states by average PM10 in 2017 as a bar chart, sorted in descending order.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2017]
    df = df.groupby('state')['PM10'].mean().reset_index().dropna()
    df = df.nlargest(15, 'PM10')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Top 15 States by Average PM10 in 2017', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2017]
     df = df.groupby('state')['PM10'].mean().reset_index().dropna()
     df = df.nlargest(15, 'PM10')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Top 15 States by Average PM10 in 2017', width=500, height=300)
     return chart


</code>
"
1424,specific_pattern,Plot the rolling 30-day average PM2.5 for Kerala in 2017 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Kerala') & (data['Timestamp'].dt.year == 2017)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Kerala 2017', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Kerala') & (data['Timestamp'].dt.year == 2017)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Kerala 2017', width=600, height=300)


</code>
"
1425,specific_pattern,Show a cumulative area chart of PM2.5 readings for Singrauli across 2021.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Singrauli') & (data['Timestamp'].dt.year == 2021)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Singrauli 2021', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Singrauli') & (data['Timestamp'].dt.year == 2021)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Singrauli 2021', width=600, height=300)
     return chart


</code>
"
1426,specific_pattern,Show a cumulative area chart of PM2.5 readings for Ooty across 2023.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Ooty') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Ooty 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Ooty') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Ooty 2023', width=600, height=300)
     return chart


</code>
"
1427,spatio_temporal_aggregation,"Create a faceted bar chart showing top 7 states by average PM2.5 per year for 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year.isin([2017,2018,2019,2020])].copy()
    df['Year'] = df['Timestamp'].dt.year
    agg = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    top = agg.groupby('Year').apply(lambda x: x.nlargest(7,'PM2.5')).reset_index(drop=True)
    chart = alt.Chart(top).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Avg PM2.5'),
        y=alt.Y('state:N', sort='-x'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).facet(facet='Year:O', columns=2).properties(title='Top 7 States by PM2.5 per Year')
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year.isin([2017,2018,2019,2020])].copy()
     df['Year'] = df['Timestamp'].dt.year
     agg = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     top = agg.groupby('Year').apply(lambda x: x.nlargest(7,'PM2.5')).reset_index(drop=True)
     chart = alt.Chart(top).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Avg PM2.5'),
         y=alt.Y('state:N', sort='-x'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).facet(facet='Year:O', columns=2).properties(title='Top 7 States by PM2.5 per Year')
     return chart


</code>
"
1428,temporal_aggregation,Show the monthly average PM10 trend for Gadag from 2017 to 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Gadag'].copy()
    df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Gadag (2017–2022)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Gadag'].copy()
     df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Gadag (2017–2022)', width=600, height=300)
     return chart


</code>
"
1429,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Odisha, Telangana, and Tripura in 2021 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Odisha', 'Telangana', 'Tripura']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Odisha, Telangana, UP – 2021', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Odisha', 'Telangana', 'Tripura']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Odisha, Telangana, UP – 2021', width=550, height=320)
     return chart


</code>
"
1430,specific_pattern,Show a cumulative area chart of PM2.5 readings for Siliguri across 2021.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Siliguri') & (data['Timestamp'].dt.year == 2021)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Siliguri 2021', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Siliguri') & (data['Timestamp'].dt.year == 2021)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Siliguri 2021', width=600, height=300)
     return chart


</code>
"
1431,temporal_aggregation,Show the monthly average PM2.5 for Nayagarh in 2019 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Nayagarh') & (data['Timestamp'].dt.year == 2019)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Nayagarh 2019', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Nayagarh') & (data['Timestamp'].dt.year == 2019)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Nayagarh 2019', width=450, height=280)


</code>
"
1432,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Tripura, Rajasthan, and Rajasthan across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Tripura', 'Rajasthan', 'Rajasthan']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Tripura', 'Rajasthan', 'Rajasthan']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
1433,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Bihar stations in 2020, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Bihar') & (data['Timestamp'].dt.year == 2020)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Bihar Stations 2020', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Bihar') & (data['Timestamp'].dt.year == 2020)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Bihar Stations 2020', width=450, height=350)


</code>
"
1434,spatial_aggregation,"Show the top 9 states by average PM10 in 2018 as a bar chart, sorted in descending order.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2018]
    df = df.groupby('state')['PM10'].mean().reset_index().dropna()
    df = df.nlargest(9, 'PM10')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Top 9 States by Average PM10 in 2018', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2018]
     df = df.groupby('state')['PM10'].mean().reset_index().dropna()
     df = df.nlargest(9, 'PM10')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Top 9 States by Average PM10 in 2018', width=500, height=300)
     return chart


</code>
"
1435,temporal_aggregation,Show the monthly average PM2.5 for Manesar in 2020 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Manesar') & (data['Timestamp'].dt.year == 2020)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Manesar 2020', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Manesar') & (data['Timestamp'].dt.year == 2020)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Manesar 2020', width=450, height=280)


</code>
"
1436,temporal_aggregation,Show the monthly average PM10 trend for Navi Mumbai from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Navi Mumbai'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Navi Mumbai (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Navi Mumbai'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Navi Mumbai (2019–2024)', width=600, height=300)
     return chart


</code>
"
1437,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for West Bengal stations in 2024, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'West Bengal') & (data['Timestamp'].dt.year == 2024)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – West Bengal Stations 2024', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'West Bengal') & (data['Timestamp'].dt.year == 2024)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – West Bengal Stations 2024', width=450, height=350)


</code>
"
1438,specific_pattern,Plot the rolling 30-day average PM2.5 for Rajasthan in 2023 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Rajasthan') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Rajasthan 2023', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Rajasthan') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Rajasthan 2023', width=600, height=300)


</code>
"
1439,spatio_temporal_aggregation,Show a heatmap of average PM2.5 for the top 12 most polluted states by month for 2024.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    top8 = data.groupby('state')['PM2.5'].mean().nlargest(12).index.tolist()
    df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_rect().encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('state:N', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
        tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Heatmap – Top 12 Polluted States by Month (2024)', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     top8 = data.groupby('state')['PM2.5'].mean().nlargest(12).index.tolist()
     df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_rect().encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('state:N', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
         tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Heatmap – Top 12 Polluted States by Month (2024)', width=500, height=300)
     return chart


</code>
"
1440,temporal_aggregation,Show a monthly bar chart of the number of days Delhi exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2022.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Delhi') & (data['Timestamp'].dt.year == 2022)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Delhi Exceeded WHO PM2.5 Guideline per Month – 2022', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Delhi') & (data['Timestamp'].dt.year == 2022)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Delhi Exceeded WHO PM2.5 Guideline per Month – 2022', width=500, height=300)
     return chart


</code>
"
1441,temporal_aggregation,Show the monthly average PM2.5 for Sirsa in 2018 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Sirsa') & (data['Timestamp'].dt.year == 2018)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Sirsa 2018', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Sirsa') & (data['Timestamp'].dt.year == 2018)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Sirsa 2018', width=450, height=280)


</code>
"
1442,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Arunachal Pradesh, Meghalaya, and Andhra Pradesh in 2018 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Arunachal Pradesh', 'Meghalaya', 'Andhra Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2018)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Arunachal Pradesh, Meghalaya, UP – 2018', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Arunachal Pradesh', 'Meghalaya', 'Andhra Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2018)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Arunachal Pradesh, Meghalaya, UP – 2018', width=550, height=320)
     return chart


</code>
"
1443,temporal_aggregation,Plot the monthly average PM2.5 trend for Nagaland from 2017 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Nagaland'].copy()
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='steelblue').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly Average PM2.5 Trend – Nagaland (2017–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Nagaland'].copy()
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='steelblue').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly Average PM2.5 Trend – Nagaland (2017–2024)', width=600, height=300)
     return chart


</code>
"
1444,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Bihar, Gujarat, and Meghalaya from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Bihar', 'Gujarat', 'Meghalaya']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Bihar vs Gujarat vs Meghalaya', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Bihar', 'Gujarat', 'Meghalaya']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Bihar vs Gujarat vs Meghalaya', width=550, height=320)
     return chart


</code>
"
1445,spatial_aggregation,Visualize the bottom 13 states with the lowest average PM2.5 in 2024 using a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2024]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nsmallest(13, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Bottom 13 States by Average PM2.5 in 2024', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2024]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nsmallest(13, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Bottom 13 States by Average PM2.5 in 2024', width=500, height=300)
     return chart


</code>
"
1446,specific_pattern,Show a cumulative area chart of PM2.5 readings for Thanjavur across 2024.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Thanjavur') & (data['Timestamp'].dt.year == 2024)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Thanjavur 2024', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Thanjavur') & (data['Timestamp'].dt.year == 2024)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Thanjavur 2024', width=600, height=300)
     return chart


</code>
"
1447,temporal_aggregation,Show a monthly bar chart of the number of days Bihar exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2023.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Bihar') & (data['Timestamp'].dt.year == 2023)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Bihar Exceeded WHO PM2.5 Guideline per Month – 2023', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Bihar') & (data['Timestamp'].dt.year == 2023)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Bihar Exceeded WHO PM2.5 Guideline per Month – 2023', width=500, height=300)
     return chart


</code>
"
1448,temporal_aggregation,Show the monthly average PM2.5 for Patna in 2020 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Patna') & (data['Timestamp'].dt.year == 2020)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Patna 2020', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Patna') & (data['Timestamp'].dt.year == 2020)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Patna 2020', width=450, height=280)


</code>
"
1449,spatial_aggregation,Plot the top 12 states by average PM2.5 in 2018 as a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2018]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nlargest(12, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 12 States by Average PM2.5 in 2018', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2018]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nlargest(12, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 12 States by Average PM2.5 in 2018', width=500, height=300)
     return chart


</code>
"
1450,spatio_temporal_aggregation,Show a heatmap of average PM2.5 for the top 11 most polluted states by month for 2023.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    top8 = data.groupby('state')['PM2.5'].mean().nlargest(11).index.tolist()
    df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_rect().encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('state:N', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
        tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Heatmap – Top 11 Polluted States by Month (2023)', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     top8 = data.groupby('state')['PM2.5'].mean().nlargest(11).index.tolist()
     df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_rect().encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('state:N', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
         tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Heatmap – Top 11 Polluted States by Month (2023)', width=500, height=300)
     return chart


</code>
"
1451,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Manipur, Sikkim, and Kerala in 2018 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Manipur', 'Sikkim', 'Kerala']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2018)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Manipur, Sikkim, UP – 2018', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Manipur', 'Sikkim', 'Kerala']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2018)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Manipur, Sikkim, UP – 2018', width=550, height=320)
     return chart


</code>
"
1452,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Chandigarh, Kerala, and Tamil Nadu from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Chandigarh', 'Kerala', 'Tamil Nadu']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Chandigarh vs Kerala vs Tamil Nadu', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Chandigarh', 'Kerala', 'Tamil Nadu']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Chandigarh vs Kerala vs Tamil Nadu', width=550, height=320)
     return chart


</code>
"
1453,temporal_aggregation,Show a monthly bar chart of the number of days Arunachal Pradesh exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2021.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Arunachal Pradesh') & (data['Timestamp'].dt.year == 2021)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Arunachal Pradesh Exceeded WHO PM2.5 Guideline per Month – 2021', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Arunachal Pradesh') & (data['Timestamp'].dt.year == 2021)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Arunachal Pradesh Exceeded WHO PM2.5 Guideline per Month – 2021', width=500, height=300)
     return chart


</code>
"
1454,spatial_aggregation,Visualize the bottom 15 states with the lowest average PM2.5 in 2023 using a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2023]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nsmallest(15, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Bottom 15 States by Average PM2.5 in 2023', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2023]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nsmallest(15, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Bottom 15 States by Average PM2.5 in 2023', width=500, height=300)
     return chart


</code>
"
1455,spatial_aggregation,Plot the top 9 states by average PM2.5 in 2024 as a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2024]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nlargest(9, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 9 States by Average PM2.5 in 2024', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2024]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nlargest(9, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 9 States by Average PM2.5 in 2024', width=500, height=300)
     return chart


</code>
"
1456,specific_pattern,Show a cumulative area chart of PM2.5 readings for Ramanagara across 2021.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Ramanagara') & (data['Timestamp'].dt.year == 2021)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Ramanagara 2021', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Ramanagara') & (data['Timestamp'].dt.year == 2021)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Ramanagara 2021', width=600, height=300)
     return chart


</code>
"
1457,temporal_aggregation,Plot the weekly average PM2.5 for Rajgir in 2021 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Rajgir') & (data['Timestamp'].dt.year == 2021)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Rajgir 2021', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Rajgir') & (data['Timestamp'].dt.year == 2021)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Rajgir 2021', width=600, height=300)
     return chart


</code>
"
1458,specific_pattern,Show a cumulative area chart of PM2.5 readings for Panchkula across 2019.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Panchkula') & (data['Timestamp'].dt.year == 2019)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Panchkula 2019', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Panchkula') & (data['Timestamp'].dt.year == 2019)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Panchkula 2019', width=600, height=300)
     return chart


</code>
"
1459,temporal_aggregation,Show the monthly average PM2.5 for Bhilwara in 2019 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Bhilwara') & (data['Timestamp'].dt.year == 2019)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Bhilwara 2019', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Bhilwara') & (data['Timestamp'].dt.year == 2019)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Bhilwara 2019', width=450, height=280)


</code>
"
1460,spatial_aggregation,Visualize the bottom 10 states with the lowest average PM2.5 in 2022 using a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2022]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nsmallest(10, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Bottom 10 States by Average PM2.5 in 2022', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2022]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nsmallest(10, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Bottom 10 States by Average PM2.5 in 2022', width=500, height=300)
     return chart


</code>
"
1461,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Himachal Pradesh, Sikkim, and Rajasthan in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Himachal Pradesh', 'Sikkim', 'Rajasthan']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Himachal Pradesh, Sikkim, UP – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Himachal Pradesh', 'Sikkim', 'Rajasthan']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Himachal Pradesh, Sikkim, UP – 2024', width=550, height=320)
     return chart


</code>
"
1462,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Rajasthan, Telangana, and Arunachal Pradesh across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Rajasthan', 'Telangana', 'Arunachal Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Rajasthan', 'Telangana', 'Arunachal Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
1463,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Jharkhand, Puducherry, and Himachal Pradesh from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Jharkhand', 'Puducherry', 'Himachal Pradesh']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Jharkhand vs Puducherry vs Himachal Pradesh', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Jharkhand', 'Puducherry', 'Himachal Pradesh']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Jharkhand vs Puducherry vs Himachal Pradesh', width=550, height=320)
     return chart


</code>
"
1464,temporal_aggregation,Show the monthly average PM2.5 for Mumbai in 2024 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Mumbai') & (data['Timestamp'].dt.year == 2024)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Mumbai 2024', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Mumbai') & (data['Timestamp'].dt.year == 2024)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Mumbai 2024', width=450, height=280)


</code>
"
1465,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Assam, Kerala, and Arunachal Pradesh across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Assam', 'Kerala', 'Arunachal Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Assam', 'Kerala', 'Arunachal Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
1466,spatio_temporal_aggregation,Show a heatmap of average PM2.5 for the top 13 most polluted states by month for 2023.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    top8 = data.groupby('state')['PM2.5'].mean().nlargest(13).index.tolist()
    df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_rect().encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('state:N', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
        tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Heatmap – Top 13 Polluted States by Month (2023)', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     top8 = data.groupby('state')['PM2.5'].mean().nlargest(13).index.tolist()
     df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_rect().encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('state:N', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
         tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Heatmap – Top 13 Polluted States by Month (2023)', width=500, height=300)
     return chart


</code>
"
1467,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Rajamahendravaram, Pratapgarh, and Sonipat in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Rajamahendravaram', 'Pratapgarh', 'Sonipat']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Rajamahendravaram vs Pratapgarh vs Sonipat – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Rajamahendravaram', 'Pratapgarh', 'Sonipat']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Rajamahendravaram vs Pratapgarh vs Sonipat – 2022', width=550, height=320)
     return chart


</code>
"
1468,temporal_aggregation,Show the monthly average PM10 trend for Rourkela from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Rourkela'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Rourkela (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Rourkela'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Rourkela (2019–2024)', width=600, height=300)
     return chart


</code>
"
1469,temporal_aggregation,Show the monthly average PM10 trend for Hajipur from 2017 to 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Hajipur'].copy()
    df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Hajipur (2017–2022)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Hajipur'].copy()
     df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Hajipur (2017–2022)', width=600, height=300)
     return chart


</code>
"
1470,spatial_aggregation,Visualize the bottom 7 states with the lowest average PM2.5 in 2023 using a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2023]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nsmallest(7, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Bottom 7 States by Average PM2.5 in 2023', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2023]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nsmallest(7, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Bottom 7 States by Average PM2.5 in 2023', width=500, height=300)
     return chart


</code>
"
1471,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Bihar stations in 2017, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Bihar') & (data['Timestamp'].dt.year == 2017)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Bihar Stations 2017', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Bihar') & (data['Timestamp'].dt.year == 2017)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Bihar Stations 2017', width=450, height=350)


</code>
"
1472,spatial_aggregation,Show a bar chart of the top 5 cities by median PM2.5 in 2024.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2024]
    df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
    df = df.nlargest(5, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
        y=alt.Y('city:N', sort='-x', title='City'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
        tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 5 Cities by Median PM2.5 in 2024', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2024]
     df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
     df = df.nlargest(5, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
         y=alt.Y('city:N', sort='-x', title='City'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
         tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 5 Cities by Median PM2.5 in 2024', width=500, height=300)
     return chart


</code>
"
1473,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Mizoram stations in 2020, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Mizoram') & (data['Timestamp'].dt.year == 2020)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Mizoram Stations 2020', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Mizoram') & (data['Timestamp'].dt.year == 2020)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Mizoram Stations 2020', width=450, height=350)


</code>
"
1474,temporal_aggregation,Show the monthly average PM10 trend for Ranipet from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Ranipet'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Ranipet (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Ranipet'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Ranipet (2019–2024)', width=600, height=300)
     return chart


</code>
"
1475,temporal_aggregation,Show a monthly bar chart of the number of days Andhra Pradesh exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2023.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Andhra Pradesh') & (data['Timestamp'].dt.year == 2023)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Andhra Pradesh Exceeded WHO PM2.5 Guideline per Month – 2023', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Andhra Pradesh') & (data['Timestamp'].dt.year == 2023)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Andhra Pradesh Exceeded WHO PM2.5 Guideline per Month – 2023', width=500, height=300)
     return chart


</code>
"
1476,temporal_aggregation,Show the monthly average PM2.5 for Ulhasnagar in 2022 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Ulhasnagar') & (data['Timestamp'].dt.year == 2022)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Ulhasnagar 2022', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Ulhasnagar') & (data['Timestamp'].dt.year == 2022)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Ulhasnagar 2022', width=450, height=280)


</code>
"
1477,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Telangana, Telangana, and Uttar Pradesh in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Telangana', 'Telangana', 'Uttar Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Telangana, Telangana, UP – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Telangana', 'Telangana', 'Uttar Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Telangana, Telangana, UP – 2024', width=550, height=320)
     return chart


</code>
"
1478,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Tripura stations in 2017, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Tripura') & (data['Timestamp'].dt.year == 2017)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Tripura Stations 2017', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Tripura') & (data['Timestamp'].dt.year == 2017)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Tripura Stations 2017', width=450, height=350)


</code>
"
1479,temporal_aggregation,Show a monthly bar chart of the number of days Chandigarh exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2020.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Chandigarh') & (data['Timestamp'].dt.year == 2020)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Chandigarh Exceeded WHO PM2.5 Guideline per Month – 2020', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Chandigarh') & (data['Timestamp'].dt.year == 2020)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Chandigarh Exceeded WHO PM2.5 Guideline per Month – 2020', width=500, height=300)
     return chart


</code>
"
1480,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Kollam, Darbhanga, and Bhiwandi in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Kollam', 'Darbhanga', 'Bhiwandi']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Kollam vs Darbhanga vs Bhiwandi – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Kollam', 'Darbhanga', 'Bhiwandi']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Kollam vs Darbhanga vs Bhiwandi – 2024', width=550, height=320)
     return chart


</code>
"
1481,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Chhattisgarh stations in 2023, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Chhattisgarh') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Chhattisgarh Stations 2023', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Chhattisgarh') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Chhattisgarh Stations 2023', width=450, height=350)


</code>
"
1482,spatial_aggregation,"Show the top 5 states by average PM10 in 2023 as a bar chart, sorted in descending order.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2023]
    df = df.groupby('state')['PM10'].mean().reset_index().dropna()
    df = df.nlargest(5, 'PM10')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Top 5 States by Average PM10 in 2023', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2023]
     df = df.groupby('state')['PM10'].mean().reset_index().dropna()
     df = df.nlargest(5, 'PM10')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Top 5 States by Average PM10 in 2023', width=500, height=300)
     return chart


</code>
"
1483,temporal_aggregation,Show the monthly average PM2.5 for Jabalpur in 2018 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Jabalpur') & (data['Timestamp'].dt.year == 2018)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Jabalpur 2018', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Jabalpur') & (data['Timestamp'].dt.year == 2018)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Jabalpur 2018', width=450, height=280)


</code>
"
1484,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Maharashtra stations in 2017, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Maharashtra') & (data['Timestamp'].dt.year == 2017)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Maharashtra Stations 2017', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Maharashtra') & (data['Timestamp'].dt.year == 2017)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Maharashtra Stations 2017', width=450, height=350)


</code>
"
1485,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Uttarakhand, Andhra Pradesh, and Uttar Pradesh from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Uttarakhand', 'Andhra Pradesh', 'Uttar Pradesh']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Uttarakhand vs Andhra Pradesh vs Uttar Pradesh', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Uttarakhand', 'Andhra Pradesh', 'Uttar Pradesh']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Uttarakhand vs Andhra Pradesh vs Uttar Pradesh', width=550, height=320)
     return chart


</code>
"
1486,temporal_aggregation,Show the monthly average PM10 trend for Panipat from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Panipat'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Panipat (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Panipat'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Panipat (2019–2024)', width=600, height=300)
     return chart


</code>
"
1487,temporal_aggregation,Plot the weekly average PM2.5 for Chittorgarh in 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Chittorgarh') & (data['Timestamp'].dt.year == 2024)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Chittorgarh 2024', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Chittorgarh') & (data['Timestamp'].dt.year == 2024)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Chittorgarh 2024', width=600, height=300)
     return chart


</code>
"
1488,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Kerala, West Bengal, and Uttarakhand in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Kerala', 'West Bengal', 'Uttarakhand']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Kerala, West Bengal, UP – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Kerala', 'West Bengal', 'Uttarakhand']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Kerala, West Bengal, UP – 2022', width=550, height=320)
     return chart


</code>
"
1489,spatial_aggregation,Visualize the bottom 12 states with the lowest average PM2.5 in 2017 using a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2017]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nsmallest(12, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Bottom 12 States by Average PM2.5 in 2017', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2017]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nsmallest(12, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Bottom 12 States by Average PM2.5 in 2017', width=500, height=300)
     return chart


</code>
"
1490,specific_pattern,Plot the rolling 30-day average PM2.5 for Jharkhand in 2020 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Jharkhand') & (data['Timestamp'].dt.year == 2020)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Jharkhand 2020', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Jharkhand') & (data['Timestamp'].dt.year == 2020)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Jharkhand 2020', width=600, height=300)


</code>
"
1491,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Nagaland, West Bengal, and Manipur across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Nagaland', 'West Bengal', 'Manipur']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Nagaland', 'West Bengal', 'Manipur']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
1492,specific_pattern,Plot the rolling 30-day average PM2.5 for Odisha in 2019 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Odisha') & (data['Timestamp'].dt.year == 2019)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Odisha 2019', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Odisha') & (data['Timestamp'].dt.year == 2019)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Odisha 2019', width=600, height=300)


</code>
"
1493,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Jalgaon, Kannur, and Ratlam in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Jalgaon', 'Kannur', 'Ratlam']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Jalgaon vs Kannur vs Ratlam – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Jalgaon', 'Kannur', 'Ratlam']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Jalgaon vs Kannur vs Ratlam – 2024', width=550, height=320)
     return chart


</code>
"
1494,specific_pattern,Show a cumulative area chart of PM2.5 readings for Agra across 2018.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Agra') & (data['Timestamp'].dt.year == 2018)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Agra 2018', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Agra') & (data['Timestamp'].dt.year == 2018)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Agra 2018', width=600, height=300)
     return chart


</code>
"
1495,specific_pattern,Show a cumulative area chart of PM2.5 readings for Sirohi across 2023.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Sirohi') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Sirohi 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Sirohi') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Sirohi 2023', width=600, height=300)
     return chart


</code>
"
1496,spatial_aggregation,Show a bar chart of the top 5 cities by median PM2.5 in 2021.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2021]
    df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
    df = df.nlargest(5, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
        y=alt.Y('city:N', sort='-x', title='City'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
        tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 5 Cities by Median PM2.5 in 2021', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2021]
     df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
     df = df.nlargest(5, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
         y=alt.Y('city:N', sort='-x', title='City'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
         tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 5 Cities by Median PM2.5 in 2021', width=500, height=300)
     return chart


</code>
"
1497,specific_pattern,Show a cumulative area chart of PM2.5 readings for Chandrapur across 2017.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Chandrapur') & (data['Timestamp'].dt.year == 2017)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Chandrapur 2017', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Chandrapur') & (data['Timestamp'].dt.year == 2017)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Chandrapur 2017', width=600, height=300)
     return chart


</code>
"
1498,temporal_aggregation,Show the monthly average PM2.5 for Aizawl in 2020 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Aizawl') & (data['Timestamp'].dt.year == 2020)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Aizawl 2020', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Aizawl') & (data['Timestamp'].dt.year == 2020)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Aizawl 2020', width=450, height=280)


</code>
"
1499,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Rajasthan, Sikkim, and Andhra Pradesh from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Rajasthan', 'Sikkim', 'Andhra Pradesh']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Rajasthan vs Sikkim vs Andhra Pradesh', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Rajasthan', 'Sikkim', 'Andhra Pradesh']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Rajasthan vs Sikkim vs Andhra Pradesh', width=550, height=320)
     return chart


</code>
"
1500,temporal_aggregation,Show the monthly average PM2.5 for Kolhapur in 2017 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Kolhapur') & (data['Timestamp'].dt.year == 2017)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Kolhapur 2017', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Kolhapur') & (data['Timestamp'].dt.year == 2017)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Kolhapur 2017', width=450, height=280)


</code>
"
1501,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Gujarat, West Bengal, and West Bengal from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Gujarat', 'West Bengal', 'West Bengal']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Gujarat vs West Bengal vs West Bengal', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Gujarat', 'West Bengal', 'West Bengal']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Gujarat vs West Bengal vs West Bengal', width=550, height=320)
     return chart


</code>
"
1502,spatio_temporal_aggregation,Show a heatmap of average PM2.5 for the top 9 most polluted states by month for 2017.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    top8 = data.groupby('state')['PM2.5'].mean().nlargest(9).index.tolist()
    df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2017)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_rect().encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('state:N', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
        tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Heatmap – Top 9 Polluted States by Month (2017)', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     top8 = data.groupby('state')['PM2.5'].mean().nlargest(9).index.tolist()
     df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2017)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_rect().encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('state:N', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
         tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Heatmap – Top 9 Polluted States by Month (2017)', width=500, height=300)
     return chart


</code>
"
1503,specific_pattern,Plot the rolling 30-day average PM2.5 for Mizoram in 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Mizoram') & (data['Timestamp'].dt.year == 2022)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Mizoram 2022', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Mizoram') & (data['Timestamp'].dt.year == 2022)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Mizoram 2022', width=600, height=300)


</code>
"
1504,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Uttar Pradesh, Rajasthan, and Manipur from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Uttar Pradesh', 'Rajasthan', 'Manipur']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Uttar Pradesh vs Rajasthan vs Manipur', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Uttar Pradesh', 'Rajasthan', 'Manipur']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Uttar Pradesh vs Rajasthan vs Manipur', width=550, height=320)
     return chart


</code>
"
1505,specific_pattern,Plot the rolling 30-day average PM2.5 for Punjab in 2017 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Punjab') & (data['Timestamp'].dt.year == 2017)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Punjab 2017', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Punjab') & (data['Timestamp'].dt.year == 2017)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Punjab 2017', width=600, height=300)


</code>
"
1506,specific_pattern,Plot the rolling 30-day average PM2.5 for Nagaland in 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Nagaland') & (data['Timestamp'].dt.year == 2022)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Nagaland 2022', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Nagaland') & (data['Timestamp'].dt.year == 2022)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Nagaland 2022', width=600, height=300)


</code>
"
1507,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Agra, Rishikesh, and Visakhapatnam in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Agra', 'Rishikesh', 'Visakhapatnam']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Agra vs Rishikesh vs Visakhapatnam – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Agra', 'Rishikesh', 'Visakhapatnam']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Agra vs Rishikesh vs Visakhapatnam – 2023', width=550, height=320)
     return chart


</code>
"
1508,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Tripura, Jammu and Kashmir, and West Bengal across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Tripura', 'Jammu and Kashmir', 'West Bengal']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Tripura', 'Jammu and Kashmir', 'West Bengal']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
1509,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Haryana, Puducherry, and Bihar across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Haryana', 'Puducherry', 'Bihar']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Haryana', 'Puducherry', 'Bihar']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
1510,spatial_aggregation,Visualize the bottom 8 states with the lowest average PM2.5 in 2023 using a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2023]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nsmallest(8, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Bottom 8 States by Average PM2.5 in 2023', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2023]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nsmallest(8, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Bottom 8 States by Average PM2.5 in 2023', width=500, height=300)
     return chart


</code>
"
1511,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Madhya Pradesh stations in 2021, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Madhya Pradesh') & (data['Timestamp'].dt.year == 2021)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Madhya Pradesh Stations 2021', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Madhya Pradesh') & (data['Timestamp'].dt.year == 2021)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Madhya Pradesh Stations 2021', width=450, height=350)


</code>
"
1512,specific_pattern,Show a cumulative area chart of PM2.5 readings for Bengaluru across 2021.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Bengaluru') & (data['Timestamp'].dt.year == 2021)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Bengaluru 2021', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Bengaluru') & (data['Timestamp'].dt.year == 2021)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Bengaluru 2021', width=600, height=300)
     return chart


</code>
"
1513,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Jaipur, Satna, and Brajrajnagar in 2018 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Jaipur', 'Satna', 'Brajrajnagar']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2018)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Jaipur vs Satna vs Brajrajnagar – 2018', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Jaipur', 'Satna', 'Brajrajnagar']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2018)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Jaipur vs Satna vs Brajrajnagar – 2018', width=550, height=320)
     return chart


</code>
"
1514,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Nagaland, Sikkim, and Madhya Pradesh in 2017 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Nagaland', 'Sikkim', 'Madhya Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2017)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Nagaland, Sikkim, UP – 2017', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Nagaland', 'Sikkim', 'Madhya Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2017)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Nagaland, Sikkim, UP – 2017', width=550, height=320)
     return chart


</code>
"
1515,spatial_aggregation,Plot the top 11 states by average PM2.5 in 2021 as a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2021]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nlargest(11, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 11 States by Average PM2.5 in 2021', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2021]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nlargest(11, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 11 States by Average PM2.5 in 2021', width=500, height=300)
     return chart


</code>
"
1516,specific_pattern,Plot the rolling 30-day average PM2.5 for Andhra Pradesh in 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Andhra Pradesh') & (data['Timestamp'].dt.year == 2024)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Andhra Pradesh 2024', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Andhra Pradesh') & (data['Timestamp'].dt.year == 2024)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Andhra Pradesh 2024', width=600, height=300)


</code>
"
1517,temporal_aggregation,Show the monthly average PM2.5 for Bilaspur in 2017 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Bilaspur') & (data['Timestamp'].dt.year == 2017)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Bilaspur 2017', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Bilaspur') & (data['Timestamp'].dt.year == 2017)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Bilaspur 2017', width=450, height=280)


</code>
"
1518,temporal_aggregation,Show the monthly average PM2.5 for Charkhi Dadri in 2022 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Charkhi Dadri') & (data['Timestamp'].dt.year == 2022)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Charkhi Dadri 2022', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Charkhi Dadri') & (data['Timestamp'].dt.year == 2022)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Charkhi Dadri 2022', width=450, height=280)


</code>
"
1519,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Rajsamand, Sikar, and Pithampur in 2019 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Rajsamand', 'Sikar', 'Pithampur']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Rajsamand vs Sikar vs Pithampur – 2019', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Rajsamand', 'Sikar', 'Pithampur']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Rajsamand vs Sikar vs Pithampur – 2019', width=550, height=320)
     return chart


</code>
"
1520,temporal_aggregation,Show the monthly average PM2.5 for Greater Noida in 2018 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Greater Noida') & (data['Timestamp'].dt.year == 2018)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Greater Noida 2018', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Greater Noida') & (data['Timestamp'].dt.year == 2018)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Greater Noida 2018', width=450, height=280)


</code>
"
1521,specific_pattern,Show a cumulative area chart of PM2.5 readings for Samastipur across 2023.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Samastipur') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Samastipur 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Samastipur') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Samastipur 2023', width=600, height=300)
     return chart


</code>
"
1522,temporal_aggregation,Show a monthly bar chart of the number of days Madhya Pradesh exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2017.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Madhya Pradesh') & (data['Timestamp'].dt.year == 2017)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Madhya Pradesh Exceeded WHO PM2.5 Guideline per Month – 2017', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Madhya Pradesh') & (data['Timestamp'].dt.year == 2017)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Madhya Pradesh Exceeded WHO PM2.5 Guideline per Month – 2017', width=500, height=300)
     return chart


</code>
"
1523,spatial_aggregation,Visualize the bottom 8 states with the lowest average PM2.5 in 2020 using a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2020]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nsmallest(8, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Bottom 8 States by Average PM2.5 in 2020', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2020]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nsmallest(8, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Bottom 8 States by Average PM2.5 in 2020', width=500, height=300)
     return chart


</code>
"
1524,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Sikkim, Rajasthan, and West Bengal in 2021 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Sikkim', 'Rajasthan', 'West Bengal']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Sikkim, Rajasthan, UP – 2021', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Sikkim', 'Rajasthan', 'West Bengal']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Sikkim, Rajasthan, UP – 2021', width=550, height=320)
     return chart


</code>
"
1525,spatial_aggregation,Show a bar chart of the top 5 cities by median PM2.5 in 2019.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2019]
    df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
    df = df.nlargest(5, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
        y=alt.Y('city:N', sort='-x', title='City'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
        tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 5 Cities by Median PM2.5 in 2019', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2019]
     df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
     df = df.nlargest(5, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
         y=alt.Y('city:N', sort='-x', title='City'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
         tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 5 Cities by Median PM2.5 in 2019', width=500, height=300)
     return chart


</code>
"
1526,temporal_aggregation,Show the monthly average PM2.5 for Mumbai in 2023 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Mumbai') & (data['Timestamp'].dt.year == 2023)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Mumbai 2023', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Mumbai') & (data['Timestamp'].dt.year == 2023)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Mumbai 2023', width=450, height=280)


</code>
"
1527,spatial_aggregation,Visualize the bottom 8 states with the lowest average PM2.5 in 2017 using a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2017]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nsmallest(8, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Bottom 8 States by Average PM2.5 in 2017', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2017]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nsmallest(8, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Bottom 8 States by Average PM2.5 in 2017', width=500, height=300)
     return chart


</code>
"
1528,temporal_aggregation,Show the monthly average PM2.5 for Dindigul in 2019 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Dindigul') & (data['Timestamp'].dt.year == 2019)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Dindigul 2019', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Dindigul') & (data['Timestamp'].dt.year == 2019)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Dindigul 2019', width=450, height=280)


</code>
"
1529,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Uttar Pradesh, Rajasthan, and Haryana from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Uttar Pradesh', 'Rajasthan', 'Haryana']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Uttar Pradesh vs Rajasthan vs Haryana', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Uttar Pradesh', 'Rajasthan', 'Haryana']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Uttar Pradesh vs Rajasthan vs Haryana', width=550, height=320)
     return chart


</code>
"
1530,temporal_aggregation,Show the monthly average PM2.5 for Dungarpur in 2017 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Dungarpur') & (data['Timestamp'].dt.year == 2017)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Dungarpur 2017', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Dungarpur') & (data['Timestamp'].dt.year == 2017)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Dungarpur 2017', width=450, height=280)


</code>
"
1531,temporal_aggregation,Plot the weekly average PM2.5 for Rohtak in 2019 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Rohtak') & (data['Timestamp'].dt.year == 2019)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Rohtak 2019', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Rohtak') & (data['Timestamp'].dt.year == 2019)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Rohtak 2019', width=600, height=300)
     return chart


</code>
"
1532,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Jharkhand, Tamil Nadu, and Puducherry in 2019 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Jharkhand', 'Tamil Nadu', 'Puducherry']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Jharkhand, Tamil Nadu, UP – 2019', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Jharkhand', 'Tamil Nadu', 'Puducherry']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Jharkhand, Tamil Nadu, UP – 2019', width=550, height=320)
     return chart


</code>
"
1533,temporal_aggregation,Show a monthly bar chart of the number of days Tamil Nadu exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2022.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Tamil Nadu') & (data['Timestamp'].dt.year == 2022)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Tamil Nadu Exceeded WHO PM2.5 Guideline per Month – 2022', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Tamil Nadu') & (data['Timestamp'].dt.year == 2022)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Tamil Nadu Exceeded WHO PM2.5 Guideline per Month – 2022', width=500, height=300)
     return chart


</code>
"
1534,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Uttarakhand, Sikkim, and Arunachal Pradesh across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Uttarakhand', 'Sikkim', 'Arunachal Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Uttarakhand', 'Sikkim', 'Arunachal Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
1535,temporal_aggregation,Show the monthly average PM2.5 for Udaipur in 2024 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Udaipur') & (data['Timestamp'].dt.year == 2024)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Udaipur 2024', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Udaipur') & (data['Timestamp'].dt.year == 2024)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Udaipur 2024', width=450, height=280)


</code>
"
1536,spatio_temporal_aggregation,Create a heatmap showing the average PM2.5 by month (x-axis) and year (y-axis) for Gujarat.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Gujarat'].copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_rect().encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Year:N', title='Year'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='Avg PM2.5'),
        tooltip=['Year:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Heatmap – Gujarat (Month × Year)', width=500, height=280)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Gujarat'].copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Year','Month'])['PM2.5'].mean().reset_index().dropna()
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_rect().encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Year:N', title='Year'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='Avg PM2.5'),
         tooltip=['Year:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Heatmap – Gujarat (Month × Year)', width=500, height=280)
     return chart


</code>
"
1537,spatial_aggregation,Visualize the bottom 8 states with the lowest average PM2.5 in 2018 using a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2018]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nsmallest(8, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Bottom 8 States by Average PM2.5 in 2018', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2018]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nsmallest(8, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Bottom 8 States by Average PM2.5 in 2018', width=500, height=300)
     return chart


</code>
"
1538,temporal_aggregation,Show the monthly average PM2.5 for Indore in 2020 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Indore') & (data['Timestamp'].dt.year == 2020)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Indore 2020', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Indore') & (data['Timestamp'].dt.year == 2020)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Indore 2020', width=450, height=280)


</code>
"
1539,spatio_temporal_aggregation,Show a heatmap of average PM2.5 for the top 13 most polluted states by month for 2019.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    top8 = data.groupby('state')['PM2.5'].mean().nlargest(13).index.tolist()
    df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_rect().encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('state:N', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
        tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Heatmap – Top 13 Polluted States by Month (2019)', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     top8 = data.groupby('state')['PM2.5'].mean().nlargest(13).index.tolist()
     df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_rect().encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('state:N', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
         tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Heatmap – Top 13 Polluted States by Month (2019)', width=500, height=300)
     return chart


</code>
"
1540,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Mizoram, Madhya Pradesh, and Arunachal Pradesh from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Mizoram', 'Madhya Pradesh', 'Arunachal Pradesh']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Mizoram vs Madhya Pradesh vs Arunachal Pradesh', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Mizoram', 'Madhya Pradesh', 'Arunachal Pradesh']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Mizoram vs Madhya Pradesh vs Arunachal Pradesh', width=550, height=320)
     return chart


</code>
"
1541,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Jharkhand, Maharashtra, and Tamil Nadu across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Jharkhand', 'Maharashtra', 'Tamil Nadu']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Jharkhand', 'Maharashtra', 'Tamil Nadu']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
1542,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Bhilai, Motihari, and Bengaluru in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Bhilai', 'Motihari', 'Bengaluru']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Bhilai vs Motihari vs Bengaluru – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Bhilai', 'Motihari', 'Bengaluru']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Bhilai vs Motihari vs Bengaluru – 2023', width=550, height=320)
     return chart


</code>
"
1543,temporal_aggregation,Show the monthly average PM2.5 for Madikeri in 2023 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Madikeri') & (data['Timestamp'].dt.year == 2023)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Madikeri 2023', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Madikeri') & (data['Timestamp'].dt.year == 2023)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Madikeri 2023', width=450, height=280)


</code>
"
1544,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Chikkamagaluru, Gadag, and Kanpur in 2019 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Chikkamagaluru', 'Gadag', 'Kanpur']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Chikkamagaluru vs Gadag vs Kanpur – 2019', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Chikkamagaluru', 'Gadag', 'Kanpur']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Chikkamagaluru vs Gadag vs Kanpur – 2019', width=550, height=320)
     return chart


</code>
"
1545,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Rajasthan, Maharashtra, and Delhi from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Rajasthan', 'Maharashtra', 'Delhi']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Rajasthan vs Maharashtra vs Delhi', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Rajasthan', 'Maharashtra', 'Delhi']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Rajasthan vs Maharashtra vs Delhi', width=550, height=320)
     return chart


</code>
"
1546,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Manipur, Tamil Nadu, and Mizoram from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Manipur', 'Tamil Nadu', 'Mizoram']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Manipur vs Tamil Nadu vs Mizoram', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Manipur', 'Tamil Nadu', 'Mizoram']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Manipur vs Tamil Nadu vs Mizoram', width=550, height=320)
     return chart


</code>
"
1547,temporal_aggregation,Show the monthly average PM2.5 for Perundurai in 2020 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Perundurai') & (data['Timestamp'].dt.year == 2020)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Perundurai 2020', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Perundurai') & (data['Timestamp'].dt.year == 2020)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Perundurai 2020', width=450, height=280)


</code>
"
1548,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Puducherry stations in 2020, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Puducherry') & (data['Timestamp'].dt.year == 2020)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Puducherry Stations 2020', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Puducherry') & (data['Timestamp'].dt.year == 2020)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Puducherry Stations 2020', width=450, height=350)


</code>
"
1549,specific_pattern,Show a cumulative area chart of PM2.5 readings for Kohima across 2022.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Kohima') & (data['Timestamp'].dt.year == 2022)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Kohima 2022', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Kohima') & (data['Timestamp'].dt.year == 2022)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Kohima 2022', width=600, height=300)
     return chart


</code>
"
1550,spatial_aggregation,"Show the top 5 states by average PM10 in 2022 as a bar chart, sorted in descending order.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2022]
    df = df.groupby('state')['PM10'].mean().reset_index().dropna()
    df = df.nlargest(5, 'PM10')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Top 5 States by Average PM10 in 2022', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2022]
     df = df.groupby('state')['PM10'].mean().reset_index().dropna()
     df = df.nlargest(5, 'PM10')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Top 5 States by Average PM10 in 2022', width=500, height=300)
     return chart


</code>
"
1551,temporal_aggregation,Show the monthly average PM2.5 for Narnaul in 2017 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Narnaul') & (data['Timestamp'].dt.year == 2017)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Narnaul 2017', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Narnaul') & (data['Timestamp'].dt.year == 2017)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Narnaul 2017', width=450, height=280)


</code>
"
1552,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Himachal Pradesh, Manipur, and Puducherry from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Himachal Pradesh', 'Manipur', 'Puducherry']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Himachal Pradesh vs Manipur vs Puducherry', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Himachal Pradesh', 'Manipur', 'Puducherry']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Himachal Pradesh vs Manipur vs Puducherry', width=550, height=320)
     return chart


</code>
"
1553,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Chhattisgarh stations in 2020, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Chhattisgarh') & (data['Timestamp'].dt.year == 2020)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Chhattisgarh Stations 2020', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Chhattisgarh') & (data['Timestamp'].dt.year == 2020)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Chhattisgarh Stations 2020', width=450, height=350)


</code>
"
1554,temporal_aggregation,Show the monthly average PM10 trend for Koppal from 2017 to 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Koppal'].copy()
    df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Koppal (2017–2022)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Koppal'].copy()
     df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Koppal (2017–2022)', width=600, height=300)
     return chart


</code>
"
1555,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Chhattisgarh, Mizoram, and Nagaland across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Chhattisgarh', 'Mizoram', 'Nagaland']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Chhattisgarh', 'Mizoram', 'Nagaland']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
1556,temporal_aggregation,Show the monthly average PM2.5 for Churu in 2017 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Churu') & (data['Timestamp'].dt.year == 2017)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Churu 2017', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Churu') & (data['Timestamp'].dt.year == 2017)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Churu 2017', width=450, height=280)


</code>
"
1557,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Uttarakhand, Jammu and Kashmir, and Mizoram in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Uttarakhand', 'Jammu and Kashmir', 'Mizoram']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Uttarakhand, Jammu and Kashmir, UP – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Uttarakhand', 'Jammu and Kashmir', 'Mizoram']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Uttarakhand, Jammu and Kashmir, UP – 2022', width=550, height=320)
     return chart


</code>
"
1558,specific_pattern,Show a cumulative area chart of PM2.5 readings for Kochi across 2024.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Kochi') & (data['Timestamp'].dt.year == 2024)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Kochi 2024', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Kochi') & (data['Timestamp'].dt.year == 2024)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Kochi 2024', width=600, height=300)
     return chart


</code>
"
1559,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Uttarakhand, Puducherry, and Uttarakhand across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Uttarakhand', 'Puducherry', 'Uttarakhand']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Uttarakhand', 'Puducherry', 'Uttarakhand']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
1560,spatial_aggregation,Visualize the bottom 7 states with the lowest average PM2.5 in 2018 using a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2018]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nsmallest(7, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Bottom 7 States by Average PM2.5 in 2018', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2018]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nsmallest(7, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Bottom 7 States by Average PM2.5 in 2018', width=500, height=300)
     return chart


</code>
"
1561,specific_pattern,Show a cumulative area chart of PM2.5 readings for Sagar across 2023.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Sagar') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Sagar 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Sagar') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Sagar 2023', width=600, height=300)
     return chart


</code>
"
1562,temporal_aggregation,Show the monthly average PM2.5 for Aurangabad in 2020 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Aurangabad') & (data['Timestamp'].dt.year == 2020)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Aurangabad 2020', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Aurangabad') & (data['Timestamp'].dt.year == 2020)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Aurangabad 2020', width=450, height=280)


</code>
"
1563,spatial_aggregation,Plot the top 6 states by average PM2.5 in 2019 as a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2019]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nlargest(6, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 6 States by Average PM2.5 in 2019', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2019]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nlargest(6, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 6 States by Average PM2.5 in 2019', width=500, height=300)
     return chart


</code>
"
1564,temporal_aggregation,Show the monthly average PM10 trend for Saharsa from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Saharsa'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Saharsa (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Saharsa'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Saharsa (2019–2024)', width=600, height=300)
     return chart


</code>
"
1565,temporal_aggregation,Plot the weekly average PM2.5 for Arrah in 2023 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Arrah') & (data['Timestamp'].dt.year == 2023)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Arrah 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Arrah') & (data['Timestamp'].dt.year == 2023)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Arrah 2023', width=600, height=300)
     return chart


</code>
"
1566,temporal_aggregation,Show the monthly average PM2.5 for Nagaur in 2018 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Nagaur') & (data['Timestamp'].dt.year == 2018)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Nagaur 2018', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Nagaur') & (data['Timestamp'].dt.year == 2018)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Nagaur 2018', width=450, height=280)


</code>
"
1567,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Andhra Pradesh, Assam, and Kerala in 2018 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Andhra Pradesh', 'Assam', 'Kerala']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2018)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Andhra Pradesh, Assam, UP – 2018', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Andhra Pradesh', 'Assam', 'Kerala']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2018)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Andhra Pradesh, Assam, UP – 2018', width=550, height=320)
     return chart


</code>
"
1568,temporal_aggregation,Show the monthly average PM2.5 for Barmer in 2023 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Barmer') & (data['Timestamp'].dt.year == 2023)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Barmer 2023', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Barmer') & (data['Timestamp'].dt.year == 2023)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Barmer 2023', width=450, height=280)


</code>
"
1569,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for West Bengal, Bihar, and Uttar Pradesh from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['West Bengal', 'Bihar', 'Uttar Pradesh']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: West Bengal vs Bihar vs Uttar Pradesh', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['West Bengal', 'Bihar', 'Uttar Pradesh']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: West Bengal vs Bihar vs Uttar Pradesh', width=550, height=320)
     return chart


</code>
"
1570,temporal_aggregation,Show the monthly average PM2.5 for Guwahati in 2022 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Guwahati') & (data['Timestamp'].dt.year == 2022)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Guwahati 2022', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Guwahati') & (data['Timestamp'].dt.year == 2022)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Guwahati 2022', width=450, height=280)


</code>
"
1571,temporal_aggregation,Plot the weekly average PM2.5 for Moradabad in 2017 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Moradabad') & (data['Timestamp'].dt.year == 2017)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Moradabad 2017', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Moradabad') & (data['Timestamp'].dt.year == 2017)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Moradabad 2017', width=600, height=300)
     return chart


</code>
"
1572,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Sikkim stations in 2019, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Sikkim') & (data['Timestamp'].dt.year == 2019)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Sikkim Stations 2019', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Sikkim') & (data['Timestamp'].dt.year == 2019)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Sikkim Stations 2019', width=450, height=350)


</code>
"
1573,temporal_aggregation,Show a monthly bar chart of the number of days West Bengal exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2020.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'West Bengal') & (data['Timestamp'].dt.year == 2020)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days West Bengal Exceeded WHO PM2.5 Guideline per Month – 2020', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'West Bengal') & (data['Timestamp'].dt.year == 2020)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days West Bengal Exceeded WHO PM2.5 Guideline per Month – 2020', width=500, height=300)
     return chart


</code>
"
1574,specific_pattern,Show a cumulative area chart of PM2.5 readings for Chhapra across 2021.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Chhapra') & (data['Timestamp'].dt.year == 2021)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Chhapra 2021', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Chhapra') & (data['Timestamp'].dt.year == 2021)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Chhapra 2021', width=600, height=300)
     return chart


</code>
"
1575,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Manipur, Punjab, and Mizoram from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Manipur', 'Punjab', 'Mizoram']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Manipur vs Punjab vs Mizoram', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Manipur', 'Punjab', 'Mizoram']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Manipur vs Punjab vs Mizoram', width=550, height=320)
     return chart


</code>
"
1576,spatial_aggregation,Plot the top 5 states by average PM2.5 in 2018 as a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2018]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nlargest(5, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 5 States by Average PM2.5 in 2018', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2018]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nlargest(5, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 5 States by Average PM2.5 in 2018', width=500, height=300)
     return chart


</code>
"
1577,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Sikkim, Kerala, and Gujarat in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Sikkim', 'Kerala', 'Gujarat']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Sikkim, Kerala, UP – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Sikkim', 'Kerala', 'Gujarat']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Sikkim, Kerala, UP – 2023', width=550, height=320)
     return chart


</code>
"
1578,temporal_aggregation,Show the monthly average PM2.5 for Nagpur in 2018 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Nagpur') & (data['Timestamp'].dt.year == 2018)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Nagpur 2018', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Nagpur') & (data['Timestamp'].dt.year == 2018)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Nagpur 2018', width=450, height=280)


</code>
"
1579,temporal_aggregation,Plot the weekly average PM2.5 for Aurangabad in 2023 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Aurangabad') & (data['Timestamp'].dt.year == 2023)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Aurangabad 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Aurangabad') & (data['Timestamp'].dt.year == 2023)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Aurangabad 2023', width=600, height=300)
     return chart


</code>
"
1580,spatio_temporal_aggregation,"Visualize the monthly average PM10 for West Bengal, Nagaland, and Madhya Pradesh in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['West Bengal', 'Nagaland', 'Madhya Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: West Bengal, Nagaland, UP – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['West Bengal', 'Nagaland', 'Madhya Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: West Bengal, Nagaland, UP – 2023', width=550, height=320)
     return chart


</code>
"
1581,spatial_aggregation,Visualize the bottom 11 states with the lowest average PM2.5 in 2022 using a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2022]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nsmallest(11, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Bottom 11 States by Average PM2.5 in 2022', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2022]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nsmallest(11, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Bottom 11 States by Average PM2.5 in 2022', width=500, height=300)
     return chart


</code>
"
1582,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Nagaland, Karnataka, and Tripura from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Nagaland', 'Karnataka', 'Tripura']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Nagaland vs Karnataka vs Tripura', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Nagaland', 'Karnataka', 'Tripura']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Nagaland vs Karnataka vs Tripura', width=550, height=320)
     return chart


</code>
"
1583,spatial_aggregation,Show a bar chart of the top 12 cities by median PM2.5 in 2024.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2024]
    df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
    df = df.nlargest(12, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
        y=alt.Y('city:N', sort='-x', title='City'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
        tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 12 Cities by Median PM2.5 in 2024', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2024]
     df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
     df = df.nlargest(12, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
         y=alt.Y('city:N', sort='-x', title='City'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
         tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 12 Cities by Median PM2.5 in 2024', width=500, height=300)
     return chart


</code>
"
1584,spatial_aggregation,Show a bar chart of the top 5 cities by median PM2.5 in 2018.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2018]
    df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
    df = df.nlargest(5, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
        y=alt.Y('city:N', sort='-x', title='City'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
        tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 5 Cities by Median PM2.5 in 2018', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2018]
     df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
     df = df.nlargest(5, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
         y=alt.Y('city:N', sort='-x', title='City'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
         tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 5 Cities by Median PM2.5 in 2018', width=500, height=300)
     return chart


</code>
"
1585,temporal_aggregation,Show a monthly bar chart of the number of days Delhi exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2023.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Delhi') & (data['Timestamp'].dt.year == 2023)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Delhi Exceeded WHO PM2.5 Guideline per Month – 2023', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Delhi') & (data['Timestamp'].dt.year == 2023)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Delhi Exceeded WHO PM2.5 Guideline per Month – 2023', width=500, height=300)
     return chart


</code>
"
1586,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Nagaland, Chhattisgarh, and Gujarat in 2021 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Nagaland', 'Chhattisgarh', 'Gujarat']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Nagaland, Chhattisgarh, UP – 2021', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Nagaland', 'Chhattisgarh', 'Gujarat']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Nagaland, Chhattisgarh, UP – 2021', width=550, height=320)
     return chart


</code>
"
1587,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Sikkim, Telangana, and Chandigarh across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Sikkim', 'Telangana', 'Chandigarh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Sikkim', 'Telangana', 'Chandigarh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
1588,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Rishikesh, Kolkata, and Saharsa in 2020 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Rishikesh', 'Kolkata', 'Saharsa']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2020)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Rishikesh vs Kolkata vs Saharsa – 2020', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Rishikesh', 'Kolkata', 'Saharsa']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2020)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Rishikesh vs Kolkata vs Saharsa – 2020', width=550, height=320)
     return chart


</code>
"
1589,temporal_aggregation,Show the monthly average PM2.5 for Akola in 2017 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Akola') & (data['Timestamp'].dt.year == 2017)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Akola 2017', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Akola') & (data['Timestamp'].dt.year == 2017)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Akola 2017', width=450, height=280)


</code>
"
1590,temporal_aggregation,Show the monthly average PM2.5 for Raipur in 2024 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Raipur') & (data['Timestamp'].dt.year == 2024)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Raipur 2024', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Raipur') & (data['Timestamp'].dt.year == 2024)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Raipur 2024', width=450, height=280)


</code>
"
1591,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Sirohi, Talcher, and Manguraha in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Sirohi', 'Talcher', 'Manguraha']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Sirohi vs Talcher vs Manguraha – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Sirohi', 'Talcher', 'Manguraha']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Sirohi vs Talcher vs Manguraha – 2024', width=550, height=320)
     return chart


</code>
"
1592,spatio_temporal_aggregation,Show a heatmap of average PM2.5 for the top 5 most polluted states by month for 2023.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    top8 = data.groupby('state')['PM2.5'].mean().nlargest(5).index.tolist()
    df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_rect().encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('state:N', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
        tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Heatmap – Top 5 Polluted States by Month (2023)', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     top8 = data.groupby('state')['PM2.5'].mean().nlargest(5).index.tolist()
     df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_rect().encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('state:N', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
         tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Heatmap – Top 5 Polluted States by Month (2023)', width=500, height=300)
     return chart


</code>
"
1593,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Arunachal Pradesh, Maharashtra, and Uttarakhand in 2018 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Arunachal Pradesh', 'Maharashtra', 'Uttarakhand']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2018)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Arunachal Pradesh, Maharashtra, UP – 2018', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Arunachal Pradesh', 'Maharashtra', 'Uttarakhand']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2018)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Arunachal Pradesh, Maharashtra, UP – 2018', width=550, height=320)
     return chart


</code>
"
1594,spatial_aggregation,"Show the top 15 states by average PM10 in 2023 as a bar chart, sorted in descending order.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2023]
    df = df.groupby('state')['PM10'].mean().reset_index().dropna()
    df = df.nlargest(15, 'PM10')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Top 15 States by Average PM10 in 2023', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2023]
     df = df.groupby('state')['PM10'].mean().reset_index().dropna()
     df = df.nlargest(15, 'PM10')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Top 15 States by Average PM10 in 2023', width=500, height=300)
     return chart


</code>
"
1595,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Jhunjhunu, Cuddalore, and Rohtak in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Jhunjhunu', 'Cuddalore', 'Rohtak']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Jhunjhunu vs Cuddalore vs Rohtak – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Jhunjhunu', 'Cuddalore', 'Rohtak']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Jhunjhunu vs Cuddalore vs Rohtak – 2022', width=550, height=320)
     return chart


</code>
"
1596,spatio_temporal_aggregation,"Visualize the monthly average PM10 for West Bengal, Mizoram, and Puducherry in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['West Bengal', 'Mizoram', 'Puducherry']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: West Bengal, Mizoram, UP – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['West Bengal', 'Mizoram', 'Puducherry']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: West Bengal, Mizoram, UP – 2022', width=550, height=320)
     return chart


</code>
"
1597,temporal_aggregation,Show a monthly bar chart of the number of days Bihar exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2017.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Bihar') & (data['Timestamp'].dt.year == 2017)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Bihar Exceeded WHO PM2.5 Guideline per Month – 2017', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Bihar') & (data['Timestamp'].dt.year == 2017)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Bihar Exceeded WHO PM2.5 Guideline per Month – 2017', width=500, height=300)
     return chart


</code>
"
1598,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Telangana, Haryana, and Kerala across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Telangana', 'Haryana', 'Kerala']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Telangana', 'Haryana', 'Kerala']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
1599,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Himachal Pradesh stations in 2023, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Himachal Pradesh') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Himachal Pradesh Stations 2023', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Himachal Pradesh') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Himachal Pradesh Stations 2023', width=450, height=350)


</code>
"
1600,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Chhattisgarh, Punjab, and Jharkhand across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Chhattisgarh', 'Punjab', 'Jharkhand']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Chhattisgarh', 'Punjab', 'Jharkhand']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
1601,temporal_aggregation,Show the monthly average PM10 trend for Singrauli from 2017 to 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Singrauli'].copy()
    df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Singrauli (2017–2022)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Singrauli'].copy()
     df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Singrauli (2017–2022)', width=600, height=300)
     return chart


</code>
"
1602,temporal_aggregation,Show the monthly average PM2.5 for Ernakulam in 2017 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Ernakulam') & (data['Timestamp'].dt.year == 2017)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Ernakulam 2017', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Ernakulam') & (data['Timestamp'].dt.year == 2017)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Ernakulam 2017', width=450, height=280)


</code>
"
1603,temporal_aggregation,Show the monthly average PM2.5 for Parbhani in 2019 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Parbhani') & (data['Timestamp'].dt.year == 2019)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Parbhani 2019', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Parbhani') & (data['Timestamp'].dt.year == 2019)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Parbhani 2019', width=450, height=280)


</code>
"
1604,specific_pattern,Plot the rolling 30-day average PM2.5 for Bihar in 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Bihar') & (data['Timestamp'].dt.year == 2024)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Bihar 2024', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Bihar') & (data['Timestamp'].dt.year == 2024)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Bihar 2024', width=600, height=300)


</code>
"
1605,spatial_aggregation,Plot the top 9 states by average PM2.5 in 2017 as a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2017]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nlargest(9, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 9 States by Average PM2.5 in 2017', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2017]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nlargest(9, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 9 States by Average PM2.5 in 2017', width=500, height=300)
     return chart


</code>
"
1606,spatial_aggregation,Plot the top 14 states by average PM2.5 in 2021 as a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2021]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nlargest(14, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 14 States by Average PM2.5 in 2021', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2021]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nlargest(14, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 14 States by Average PM2.5 in 2021', width=500, height=300)
     return chart


</code>
"
1607,specific_pattern,Plot the rolling 30-day average PM2.5 for Jammu and Kashmir in 2019 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Jammu and Kashmir') & (data['Timestamp'].dt.year == 2019)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Jammu and Kashmir 2019', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Jammu and Kashmir') & (data['Timestamp'].dt.year == 2019)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Jammu and Kashmir 2019', width=600, height=300)


</code>
"
1608,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Karnataka, Puducherry, and Nagaland in 2019 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Karnataka', 'Puducherry', 'Nagaland']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Karnataka, Puducherry, UP – 2019', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Karnataka', 'Puducherry', 'Nagaland']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Karnataka, Puducherry, UP – 2019', width=550, height=320)
     return chart


</code>
"
1609,temporal_aggregation,Show the monthly average PM10 trend for Chandigarh from 2017 to 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Chandigarh'].copy()
    df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Chandigarh (2017–2022)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Chandigarh'].copy()
     df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Chandigarh (2017–2022)', width=600, height=300)
     return chart


</code>
"
1610,spatio_temporal_aggregation,Show a heatmap of average PM2.5 for the top 14 most polluted states by month for 2021.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    top8 = data.groupby('state')['PM2.5'].mean().nlargest(14).index.tolist()
    df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2021)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_rect().encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('state:N', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
        tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Heatmap – Top 14 Polluted States by Month (2021)', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     top8 = data.groupby('state')['PM2.5'].mean().nlargest(14).index.tolist()
     df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2021)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_rect().encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('state:N', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
         tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Heatmap – Top 14 Polluted States by Month (2021)', width=500, height=300)
     return chart


</code>
"
1611,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Delhi stations in 2023, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Delhi') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Delhi Stations 2023', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Delhi') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Delhi Stations 2023', width=450, height=350)


</code>
"
1612,spatial_aggregation,"Show the top 10 states by average PM10 in 2023 as a bar chart, sorted in descending order.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2023]
    df = df.groupby('state')['PM10'].mean().reset_index().dropna()
    df = df.nlargest(10, 'PM10')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Top 10 States by Average PM10 in 2023', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2023]
     df = df.groupby('state')['PM10'].mean().reset_index().dropna()
     df = df.nlargest(10, 'PM10')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Top 10 States by Average PM10 in 2023', width=500, height=300)
     return chart


</code>
"
1613,specific_pattern,Plot the rolling 30-day average PM2.5 for Gujarat in 2020 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Gujarat') & (data['Timestamp'].dt.year == 2020)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Gujarat 2020', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Gujarat') & (data['Timestamp'].dt.year == 2020)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Gujarat 2020', width=600, height=300)


</code>
"
1614,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Puducherry, Punjab, and Tripura across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Puducherry', 'Punjab', 'Tripura']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Puducherry', 'Punjab', 'Tripura']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
1615,temporal_aggregation,Show the monthly average PM10 trend for Bhiwadi from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Bhiwadi'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Bhiwadi (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Bhiwadi'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Bhiwadi (2019–2024)', width=600, height=300)
     return chart


</code>
"
1616,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Chhattisgarh, Delhi, and Sikkim in 2018 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Chhattisgarh', 'Delhi', 'Sikkim']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2018)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Chhattisgarh, Delhi, UP – 2018', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Chhattisgarh', 'Delhi', 'Sikkim']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2018)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Chhattisgarh, Delhi, UP – 2018', width=550, height=320)
     return chart


</code>
"
1617,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Chikkaballapur, Chandrapur, and Sawai Madhopur in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Chikkaballapur', 'Chandrapur', 'Sawai Madhopur']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Chikkaballapur vs Chandrapur vs Sawai Madhopur – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Chikkaballapur', 'Chandrapur', 'Sawai Madhopur']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Chikkaballapur vs Chandrapur vs Sawai Madhopur – 2023', width=550, height=320)
     return chart


</code>
"
1618,specific_pattern,Show a cumulative area chart of PM2.5 readings for Hisar across 2019.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Hisar') & (data['Timestamp'].dt.year == 2019)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Hisar 2019', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Hisar') & (data['Timestamp'].dt.year == 2019)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Hisar 2019', width=600, height=300)
     return chart


</code>
"
1619,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Gujarat, Bihar, and Haryana across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Gujarat', 'Bihar', 'Haryana']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Gujarat', 'Bihar', 'Haryana']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
1620,temporal_aggregation,Show the monthly average PM2.5 for Nashik in 2024 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Nashik') & (data['Timestamp'].dt.year == 2024)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Nashik 2024', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Nashik') & (data['Timestamp'].dt.year == 2024)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Nashik 2024', width=450, height=280)


</code>
"
1621,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Sikkim, Tamil Nadu, and Jharkhand across 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Sikkim', 'Tamil Nadu', 'Jharkhand']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Sikkim', 'Tamil Nadu', 'Jharkhand']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
     return chart


</code>
"
1622,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Bihar, Andhra Pradesh, and Karnataka from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Bihar', 'Andhra Pradesh', 'Karnataka']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Bihar vs Andhra Pradesh vs Karnataka', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Bihar', 'Andhra Pradesh', 'Karnataka']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Bihar vs Andhra Pradesh vs Karnataka', width=550, height=320)
     return chart


</code>
"
1623,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Madhya Pradesh, Madhya Pradesh, and Puducherry from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Madhya Pradesh', 'Madhya Pradesh', 'Puducherry']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Madhya Pradesh vs Madhya Pradesh vs Puducherry', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Madhya Pradesh', 'Madhya Pradesh', 'Puducherry']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Madhya Pradesh vs Madhya Pradesh vs Puducherry', width=550, height=320)
     return chart


</code>
"
1624,specific_pattern,Show PM2.5 exceedances above the Indian standard (60 µg/m³) per year as a bar chart for Uttarakhand.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Uttarakhand'].dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 60].copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby('Year')['Timestamp'].nunique().reset_index()
    df.columns = ['Year','Days Exceeded']
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('Days Exceeded:Q', title='Days PM2.5 > 60 µg/m³'),
        tooltip=['Year:N','Days Exceeded:Q']
    ).properties(title='Days Uttarakhand Exceeded Indian PM2.5 Standard (60 µg/m³) per Year', width=450, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Uttarakhand'].dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 60].copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby('Year')['Timestamp'].nunique().reset_index()
     df.columns = ['Year','Days Exceeded']
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('Days Exceeded:Q', title='Days PM2.5 > 60 µg/m³'),
         tooltip=['Year:N','Days Exceeded:Q']
     ).properties(title='Days Uttarakhand Exceeded Indian PM2.5 Standard (60 µg/m³) per Year', width=450, height=300)
     return chart


</code>
"
1625,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Jharkhand, Karnataka, and Sikkim across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Jharkhand', 'Karnataka', 'Sikkim']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Jharkhand', 'Karnataka', 'Sikkim']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
1626,temporal_aggregation,Show the monthly average PM10 trend for Banswara from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Banswara'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Banswara (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Banswara'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Banswara (2019–2024)', width=600, height=300)
     return chart


</code>
"
1627,temporal_aggregation,Plot the weekly average PM2.5 for Gaya in 2018 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Gaya') & (data['Timestamp'].dt.year == 2018)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Gaya 2018', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Gaya') & (data['Timestamp'].dt.year == 2018)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Gaya 2018', width=600, height=300)
     return chart


</code>
"
1628,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Odisha, Karnataka, and Rajasthan across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Odisha', 'Karnataka', 'Rajasthan']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Odisha', 'Karnataka', 'Rajasthan']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
1629,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Gujarat, Bihar, and Karnataka in 2017 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Gujarat', 'Bihar', 'Karnataka']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2017)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Gujarat, Bihar, UP – 2017', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Gujarat', 'Bihar', 'Karnataka']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2017)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Gujarat, Bihar, UP – 2017', width=550, height=320)
     return chart


</code>
"
1630,specific_pattern,Show a cumulative area chart of PM2.5 readings for Bhiwandi across 2024.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Bhiwandi') & (data['Timestamp'].dt.year == 2024)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Bhiwandi 2024', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Bhiwandi') & (data['Timestamp'].dt.year == 2024)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Bhiwandi 2024', width=600, height=300)
     return chart


</code>
"
1631,spatial_aggregation,Plot the top 6 states by average PM2.5 in 2017 as a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2017]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nlargest(6, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 6 States by Average PM2.5 in 2017', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2017]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nlargest(6, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 6 States by Average PM2.5 in 2017', width=500, height=300)
     return chart


</code>
"
1632,spatial_aggregation,"Show the top 14 states by average PM10 in 2022 as a bar chart, sorted in descending order.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2022]
    df = df.groupby('state')['PM10'].mean().reset_index().dropna()
    df = df.nlargest(14, 'PM10')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Top 14 States by Average PM10 in 2022', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2022]
     df = df.groupby('state')['PM10'].mean().reset_index().dropna()
     df = df.nlargest(14, 'PM10')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Top 14 States by Average PM10 in 2022', width=500, height=300)
     return chart


</code>
"
1633,specific_pattern,Show a cumulative area chart of PM2.5 readings for Kochi across 2021.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Kochi') & (data['Timestamp'].dt.year == 2021)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Kochi 2021', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Kochi') & (data['Timestamp'].dt.year == 2021)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Kochi 2021', width=600, height=300)
     return chart


</code>
"
1634,specific_pattern,Show a cumulative area chart of PM2.5 readings for Noida across 2017.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Noida') & (data['Timestamp'].dt.year == 2017)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Noida 2017', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Noida') & (data['Timestamp'].dt.year == 2017)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Noida 2017', width=600, height=300)
     return chart


</code>
"
1635,temporal_aggregation,Plot the weekly average PM2.5 for Byrnihat in 2023 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Byrnihat') & (data['Timestamp'].dt.year == 2023)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Byrnihat 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Byrnihat') & (data['Timestamp'].dt.year == 2023)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Byrnihat 2023', width=600, height=300)
     return chart


</code>
"
1636,spatial_aggregation,Plot the top 12 states by average PM2.5 in 2022 as a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2022]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nlargest(12, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 12 States by Average PM2.5 in 2022', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2022]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nlargest(12, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 12 States by Average PM2.5 in 2022', width=500, height=300)
     return chart


</code>
"
1637,temporal_aggregation,Plot the weekly average PM2.5 for Delhi in 2023 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Delhi') & (data['Timestamp'].dt.year == 2023)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Delhi 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Delhi') & (data['Timestamp'].dt.year == 2023)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Delhi 2023', width=600, height=300)
     return chart


</code>
"
1638,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Chandrapur, Bhilwara, and Arrah in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Chandrapur', 'Bhilwara', 'Arrah']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Chandrapur vs Bhilwara vs Arrah – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Chandrapur', 'Bhilwara', 'Arrah']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Chandrapur vs Bhilwara vs Arrah – 2024', width=550, height=320)
     return chart


</code>
"
1639,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Gujarat, Arunachal Pradesh, and Himachal Pradesh across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Gujarat', 'Arunachal Pradesh', 'Himachal Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Gujarat', 'Arunachal Pradesh', 'Himachal Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
1640,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Chandigarh, Delhi, and Himachal Pradesh in 2017 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Chandigarh', 'Delhi', 'Himachal Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2017)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Chandigarh, Delhi, UP – 2017', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Chandigarh', 'Delhi', 'Himachal Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2017)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Chandigarh, Delhi, UP – 2017', width=550, height=320)
     return chart


</code>
"
1641,temporal_aggregation,Plot the weekly average PM2.5 for Motihari in 2021 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Motihari') & (data['Timestamp'].dt.year == 2021)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Motihari 2021', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Motihari') & (data['Timestamp'].dt.year == 2021)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Motihari 2021', width=600, height=300)
     return chart


</code>
"
1642,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Rohtak, Palkalaiperur, and Dewas in 2020 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Rohtak', 'Palkalaiperur', 'Dewas']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2020)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Rohtak vs Palkalaiperur vs Dewas – 2020', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Rohtak', 'Palkalaiperur', 'Dewas']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2020)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Rohtak vs Palkalaiperur vs Dewas – 2020', width=550, height=320)
     return chart


</code>
"
1643,specific_pattern,Show a cumulative area chart of PM2.5 readings for Udaipur across 2018.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Udaipur') & (data['Timestamp'].dt.year == 2018)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Udaipur 2018', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Udaipur') & (data['Timestamp'].dt.year == 2018)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Udaipur 2018', width=600, height=300)
     return chart


</code>
"
1644,temporal_aggregation,Show the monthly average PM2.5 for Nagapattinam in 2023 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Nagapattinam') & (data['Timestamp'].dt.year == 2023)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Nagapattinam 2023', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Nagapattinam') & (data['Timestamp'].dt.year == 2023)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Nagapattinam 2023', width=450, height=280)


</code>
"
1645,temporal_aggregation,Show a monthly bar chart of the number of days Telangana exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2021.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Telangana') & (data['Timestamp'].dt.year == 2021)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Telangana Exceeded WHO PM2.5 Guideline per Month – 2021', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Telangana') & (data['Timestamp'].dt.year == 2021)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Telangana Exceeded WHO PM2.5 Guideline per Month – 2021', width=500, height=300)
     return chart


</code>
"
1646,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Andhra Pradesh, Nagaland, and West Bengal across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Andhra Pradesh', 'Nagaland', 'West Bengal']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Andhra Pradesh', 'Nagaland', 'West Bengal']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
1647,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Bihar, Haryana, and Meghalaya in 2017 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Bihar', 'Haryana', 'Meghalaya']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2017)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Bihar, Haryana, UP – 2017', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Bihar', 'Haryana', 'Meghalaya']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2017)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Bihar, Haryana, UP – 2017', width=550, height=320)
     return chart


</code>
"
1648,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Haryana, Bihar, and Andhra Pradesh from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Haryana', 'Bihar', 'Andhra Pradesh']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Haryana vs Bihar vs Andhra Pradesh', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Haryana', 'Bihar', 'Andhra Pradesh']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Haryana vs Bihar vs Andhra Pradesh', width=550, height=320)
     return chart


</code>
"
1649,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Charkhi Dadri, Dausa, and Thane in 2017 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Charkhi Dadri', 'Dausa', 'Thane']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2017)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Charkhi Dadri vs Dausa vs Thane – 2017', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Charkhi Dadri', 'Dausa', 'Thane']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2017)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Charkhi Dadri vs Dausa vs Thane – 2017', width=550, height=320)
     return chart


</code>
"
1650,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Kerala, Nagaland, and Punjab in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Kerala', 'Nagaland', 'Punjab']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Kerala, Nagaland, UP – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Kerala', 'Nagaland', 'Punjab']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Kerala, Nagaland, UP – 2023', width=550, height=320)
     return chart


</code>
"
1651,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Himachal Pradesh, Tripura, and Odisha across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Himachal Pradesh', 'Tripura', 'Odisha']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Himachal Pradesh', 'Tripura', 'Odisha']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
1652,temporal_aggregation,Show the monthly average PM10 trend for Solapur from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Solapur'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Solapur (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Solapur'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Solapur (2019–2024)', width=600, height=300)
     return chart


</code>
"
1653,specific_pattern,Show a cumulative area chart of PM2.5 readings for Manesar across 2022.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Manesar') & (data['Timestamp'].dt.year == 2022)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Manesar 2022', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Manesar') & (data['Timestamp'].dt.year == 2022)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Manesar 2022', width=600, height=300)
     return chart


</code>
"
1654,spatio_temporal_aggregation,"Visualize the monthly average PM10 for West Bengal, Puducherry, and Uttarakhand in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['West Bengal', 'Puducherry', 'Uttarakhand']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: West Bengal, Puducherry, UP – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['West Bengal', 'Puducherry', 'Uttarakhand']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: West Bengal, Puducherry, UP – 2023', width=550, height=320)
     return chart


</code>
"
1655,spatial_aggregation,Show a bar chart of the top 7 cities by median PM2.5 in 2019.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2019]
    df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
    df = df.nlargest(7, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
        y=alt.Y('city:N', sort='-x', title='City'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
        tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 7 Cities by Median PM2.5 in 2019', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2019]
     df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
     df = df.nlargest(7, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
         y=alt.Y('city:N', sort='-x', title='City'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
         tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 7 Cities by Median PM2.5 in 2019', width=500, height=300)
     return chart


</code>
"
1656,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Tripura, Meghalaya, and Sikkim in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Tripura', 'Meghalaya', 'Sikkim']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Tripura, Meghalaya, UP – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Tripura', 'Meghalaya', 'Sikkim']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Tripura, Meghalaya, UP – 2024', width=550, height=320)
     return chart


</code>
"
1657,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Hapur, Noida, and Agra in 2018 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Hapur', 'Noida', 'Agra']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2018)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Hapur vs Noida vs Agra – 2018', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Hapur', 'Noida', 'Agra']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2018)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Hapur vs Noida vs Agra – 2018', width=550, height=320)
     return chart


</code>
"
1658,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Jharkhand, Jharkhand, and Karnataka from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Jharkhand', 'Jharkhand', 'Karnataka']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Jharkhand vs Jharkhand vs Karnataka', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Jharkhand', 'Jharkhand', 'Karnataka']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Jharkhand vs Jharkhand vs Karnataka', width=550, height=320)
     return chart


</code>
"
1659,temporal_aggregation,Show the monthly average PM10 trend for Raichur from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Raichur'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Raichur (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Raichur'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Raichur (2019–2024)', width=600, height=300)
     return chart


</code>
"
1660,temporal_aggregation,Show the monthly average PM10 trend for Pithampur from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Pithampur'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Pithampur (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Pithampur'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Pithampur (2019–2024)', width=600, height=300)
     return chart


</code>
"
1661,specific_pattern,Plot the rolling 30-day average PM2.5 for Chandigarh in 2023 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Chandigarh') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Chandigarh 2023', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Chandigarh') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Chandigarh 2023', width=600, height=300)


</code>
"
1662,temporal_aggregation,Show the monthly average PM10 trend for Manesar from 2017 to 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Manesar'].copy()
    df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Manesar (2017–2022)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Manesar'].copy()
     df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Manesar (2017–2022)', width=600, height=300)
     return chart


</code>
"
1663,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Kerala, Himachal Pradesh, and Himachal Pradesh across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Kerala', 'Himachal Pradesh', 'Himachal Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Kerala', 'Himachal Pradesh', 'Himachal Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
1664,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Nagaland, Rajasthan, and Chandigarh across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Nagaland', 'Rajasthan', 'Chandigarh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Nagaland', 'Rajasthan', 'Chandigarh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
1665,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Bihar, Sikkim, and Tripura in 2019 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Bihar', 'Sikkim', 'Tripura']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Bihar, Sikkim, UP – 2019', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Bihar', 'Sikkim', 'Tripura']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Bihar, Sikkim, UP – 2019', width=550, height=320)
     return chart


</code>
"
1666,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Ghaziabad, Pudukottai, and Jalna in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Ghaziabad', 'Pudukottai', 'Jalna']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Ghaziabad vs Pudukottai vs Jalna – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Ghaziabad', 'Pudukottai', 'Jalna']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Ghaziabad vs Pudukottai vs Jalna – 2023', width=550, height=320)
     return chart


</code>
"
1667,specific_pattern,Show a cumulative area chart of PM2.5 readings for Srinagar across 2023.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Srinagar') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Srinagar 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Srinagar') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Srinagar 2023', width=600, height=300)
     return chart


</code>
"
1668,temporal_aggregation,Show the monthly average PM2.5 for Cuttack in 2024 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Cuttack') & (data['Timestamp'].dt.year == 2024)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Cuttack 2024', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Cuttack') & (data['Timestamp'].dt.year == 2024)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Cuttack 2024', width=450, height=280)


</code>
"
1669,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Bihar, Karnataka, and Chandigarh from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Bihar', 'Karnataka', 'Chandigarh']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Bihar vs Karnataka vs Chandigarh', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Bihar', 'Karnataka', 'Chandigarh']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Bihar vs Karnataka vs Chandigarh', width=550, height=320)
     return chart


</code>
"
1670,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Nagaland, Assam, and Delhi across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Nagaland', 'Assam', 'Delhi']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Nagaland', 'Assam', 'Delhi']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
1671,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Himachal Pradesh stations in 2021, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Himachal Pradesh') & (data['Timestamp'].dt.year == 2021)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Himachal Pradesh Stations 2021', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Himachal Pradesh') & (data['Timestamp'].dt.year == 2021)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Himachal Pradesh Stations 2021', width=450, height=350)


</code>
"
1672,specific_pattern,Show a cumulative area chart of PM2.5 readings for Hyderabad across 2023.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Hyderabad') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Hyderabad 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Hyderabad') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Hyderabad 2023', width=600, height=300)
     return chart


</code>
"
1673,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Jammu and Kashmir, Maharashtra, and Kerala from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Jammu and Kashmir', 'Maharashtra', 'Kerala']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Jammu and Kashmir vs Maharashtra vs Kerala', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Jammu and Kashmir', 'Maharashtra', 'Kerala']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Jammu and Kashmir vs Maharashtra vs Kerala', width=550, height=320)
     return chart


</code>
"
1674,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Himachal Pradesh, Haryana, and Bihar across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Himachal Pradesh', 'Haryana', 'Bihar']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Himachal Pradesh', 'Haryana', 'Bihar']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
1675,spatio_temporal_aggregation,Show a heatmap of average PM2.5 for the top 10 most polluted states by month for 2019.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    top8 = data.groupby('state')['PM2.5'].mean().nlargest(10).index.tolist()
    df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_rect().encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('state:N', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
        tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Heatmap – Top 10 Polluted States by Month (2019)', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     top8 = data.groupby('state')['PM2.5'].mean().nlargest(10).index.tolist()
     df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_rect().encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('state:N', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
         tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Heatmap – Top 10 Polluted States by Month (2019)', width=500, height=300)
     return chart


</code>
"
1676,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Kolhapur, Maihar, and Muzaffarnagar in 2020 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Kolhapur', 'Maihar', 'Muzaffarnagar']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2020)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Kolhapur vs Maihar vs Muzaffarnagar – 2020', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Kolhapur', 'Maihar', 'Muzaffarnagar']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2020)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Kolhapur vs Maihar vs Muzaffarnagar – 2020', width=550, height=320)
     return chart


</code>
"
1677,temporal_aggregation,Show the monthly average PM10 trend for Mumbai from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Mumbai'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Mumbai (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Mumbai'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Mumbai (2019–2024)', width=600, height=300)
     return chart


</code>
"
1678,spatio_temporal_aggregation,Show a heatmap of average PM2.5 for the top 15 most polluted states by month for 2018.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    top8 = data.groupby('state')['PM2.5'].mean().nlargest(15).index.tolist()
    df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2018)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_rect().encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('state:N', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
        tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Heatmap – Top 15 Polluted States by Month (2018)', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     top8 = data.groupby('state')['PM2.5'].mean().nlargest(15).index.tolist()
     df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2018)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_rect().encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('state:N', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
         tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Heatmap – Top 15 Polluted States by Month (2018)', width=500, height=300)
     return chart


</code>
"
1679,temporal_aggregation,Show the monthly average PM2.5 for Ramanagara in 2018 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Ramanagara') & (data['Timestamp'].dt.year == 2018)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Ramanagara 2018', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Ramanagara') & (data['Timestamp'].dt.year == 2018)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Ramanagara 2018', width=450, height=280)


</code>
"
1680,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Punjab stations in 2021, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Punjab') & (data['Timestamp'].dt.year == 2021)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Punjab Stations 2021', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Punjab') & (data['Timestamp'].dt.year == 2021)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Punjab Stations 2021', width=450, height=350)


</code>
"
1681,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Muzaffarpur, Mangalore, and Visakhapatnam in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Muzaffarpur', 'Mangalore', 'Visakhapatnam']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Muzaffarpur vs Mangalore vs Visakhapatnam – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Muzaffarpur', 'Mangalore', 'Visakhapatnam']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Muzaffarpur vs Mangalore vs Visakhapatnam – 2024', width=550, height=320)
     return chart


</code>
"
1682,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Manipur, Manipur, and Kerala across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Manipur', 'Manipur', 'Kerala']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Manipur', 'Manipur', 'Kerala']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
1683,specific_pattern,Show a cumulative area chart of PM2.5 readings for Pali across 2018.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Pali') & (data['Timestamp'].dt.year == 2018)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Pali 2018', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Pali') & (data['Timestamp'].dt.year == 2018)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Pali 2018', width=600, height=300)
     return chart


</code>
"
1684,temporal_aggregation,Plot the weekly average PM2.5 for Agra in 2018 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Agra') & (data['Timestamp'].dt.year == 2018)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Agra 2018', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Agra') & (data['Timestamp'].dt.year == 2018)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Agra 2018', width=600, height=300)
     return chart


</code>
"
1685,spatial_aggregation,Plot the top 13 states by average PM2.5 in 2024 as a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2024]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nlargest(13, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 13 States by Average PM2.5 in 2024', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2024]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nlargest(13, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 13 States by Average PM2.5 in 2024', width=500, height=300)
     return chart


</code>
"
1686,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Arunachal Pradesh, Gujarat, and Meghalaya from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Arunachal Pradesh', 'Gujarat', 'Meghalaya']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Arunachal Pradesh vs Gujarat vs Meghalaya', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Arunachal Pradesh', 'Gujarat', 'Meghalaya']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Arunachal Pradesh vs Gujarat vs Meghalaya', width=550, height=320)
     return chart


</code>
"
1687,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Meghalaya, Delhi, and Bihar in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Meghalaya', 'Delhi', 'Bihar']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Meghalaya, Delhi, UP – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Meghalaya', 'Delhi', 'Bihar']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Meghalaya, Delhi, UP – 2024', width=550, height=320)
     return chart


</code>
"
1688,temporal_aggregation,Show the monthly average PM10 trend for Khurja from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Khurja'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Khurja (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Khurja'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Khurja (2019–2024)', width=600, height=300)
     return chart


</code>
"
1689,spatial_aggregation,Plot the top 10 states by average PM2.5 in 2024 as a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2024]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nlargest(10, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 10 States by Average PM2.5 in 2024', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2024]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nlargest(10, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 10 States by Average PM2.5 in 2024', width=500, height=300)
     return chart


</code>
"
1690,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Rajasthan, Haryana, and West Bengal from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Rajasthan', 'Haryana', 'West Bengal']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Rajasthan vs Haryana vs West Bengal', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Rajasthan', 'Haryana', 'West Bengal']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Rajasthan vs Haryana vs West Bengal', width=550, height=320)
     return chart


</code>
"
1691,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Rajgir, Gurugram, and Vijayapura in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Rajgir', 'Gurugram', 'Vijayapura']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Rajgir vs Gurugram vs Vijayapura – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Rajgir', 'Gurugram', 'Vijayapura']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Rajgir vs Gurugram vs Vijayapura – 2024', width=550, height=320)
     return chart


</code>
"
1692,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Jammu and Kashmir, Delhi, and Haryana from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Jammu and Kashmir', 'Delhi', 'Haryana']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Jammu and Kashmir vs Delhi vs Haryana', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Jammu and Kashmir', 'Delhi', 'Haryana']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Jammu and Kashmir vs Delhi vs Haryana', width=550, height=320)
     return chart


</code>
"
1693,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Chandigarh, Maharashtra, and Uttar Pradesh across 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Chandigarh', 'Maharashtra', 'Uttar Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Chandigarh', 'Maharashtra', 'Uttar Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
     return chart


</code>
"
1694,temporal_aggregation,Show the monthly average PM2.5 for Sirsa in 2022 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Sirsa') & (data['Timestamp'].dt.year == 2022)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Sirsa 2022', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Sirsa') & (data['Timestamp'].dt.year == 2022)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Sirsa 2022', width=450, height=280)


</code>
"
1695,spatial_aggregation,Show a bar chart of the top 6 cities by median PM2.5 in 2018.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2018]
    df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
    df = df.nlargest(6, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
        y=alt.Y('city:N', sort='-x', title='City'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
        tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 6 Cities by Median PM2.5 in 2018', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2018]
     df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
     df = df.nlargest(6, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
         y=alt.Y('city:N', sort='-x', title='City'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
         tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 6 Cities by Median PM2.5 in 2018', width=500, height=300)
     return chart


</code>
"
1696,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Madhya Pradesh, Odisha, and Uttarakhand across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Madhya Pradesh', 'Odisha', 'Uttarakhand']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Madhya Pradesh', 'Odisha', 'Uttarakhand']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
1697,spatial_aggregation,Plot the top 5 states by average PM2.5 in 2024 as a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2024]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nlargest(5, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 5 States by Average PM2.5 in 2024', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2024]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nlargest(5, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 5 States by Average PM2.5 in 2024', width=500, height=300)
     return chart


</code>
"
1698,temporal_aggregation,Show the monthly average PM10 trend for Yadgir from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Yadgir'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Yadgir (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Yadgir'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Yadgir (2019–2024)', width=600, height=300)
     return chart


</code>
"
1699,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Punjab, Meghalaya, and Haryana in 2017 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Punjab', 'Meghalaya', 'Haryana']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2017)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Punjab, Meghalaya, UP – 2017', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Punjab', 'Meghalaya', 'Haryana']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2017)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Punjab, Meghalaya, UP – 2017', width=550, height=320)
     return chart


</code>
"
1700,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Andhra Pradesh stations in 2018, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Andhra Pradesh') & (data['Timestamp'].dt.year == 2018)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Andhra Pradesh Stations 2018', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Andhra Pradesh') & (data['Timestamp'].dt.year == 2018)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Andhra Pradesh Stations 2018', width=450, height=350)


</code>
"
1701,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Andhra Pradesh, Kerala, and Gujarat from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Andhra Pradesh', 'Kerala', 'Gujarat']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Andhra Pradesh vs Kerala vs Gujarat', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Andhra Pradesh', 'Kerala', 'Gujarat']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Andhra Pradesh vs Kerala vs Gujarat', width=550, height=320)
     return chart


</code>
"
1702,spatial_aggregation,Show a bar chart of the top 10 cities by median PM2.5 in 2021.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2021]
    df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
    df = df.nlargest(10, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
        y=alt.Y('city:N', sort='-x', title='City'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
        tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 10 Cities by Median PM2.5 in 2021', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2021]
     df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
     df = df.nlargest(10, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
         y=alt.Y('city:N', sort='-x', title='City'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
         tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 10 Cities by Median PM2.5 in 2021', width=500, height=300)
     return chart


</code>
"
1703,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Nagaland, Puducherry, and Chhattisgarh across 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Nagaland', 'Puducherry', 'Chhattisgarh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Nagaland', 'Puducherry', 'Chhattisgarh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
     return chart


</code>
"
1704,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Uttar Pradesh, Himachal Pradesh, and Puducherry across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Uttar Pradesh', 'Himachal Pradesh', 'Puducherry']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Uttar Pradesh', 'Himachal Pradesh', 'Puducherry']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
1705,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Chandigarh, Jammu and Kashmir, and Assam across 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Chandigarh', 'Jammu and Kashmir', 'Assam']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Chandigarh', 'Jammu and Kashmir', 'Assam']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
     return chart


</code>
"
1706,temporal_aggregation,Show the monthly average PM10 trend for Narnaul from 2017 to 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Narnaul'].copy()
    df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Narnaul (2017–2022)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Narnaul'].copy()
     df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Narnaul (2017–2022)', width=600, height=300)
     return chart


</code>
"
1707,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Punjab, Nagaland, and Kerala across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Punjab', 'Nagaland', 'Kerala']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Punjab', 'Nagaland', 'Kerala']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
1708,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Mizoram stations in 2021, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Mizoram') & (data['Timestamp'].dt.year == 2021)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Mizoram Stations 2021', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Mizoram') & (data['Timestamp'].dt.year == 2021)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Mizoram Stations 2021', width=450, height=350)


</code>
"
1709,specific_pattern,Show a cumulative area chart of PM2.5 readings for Hassan across 2024.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Hassan') & (data['Timestamp'].dt.year == 2024)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Hassan 2024', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Hassan') & (data['Timestamp'].dt.year == 2024)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Hassan 2024', width=600, height=300)
     return chart


</code>
"
1710,spatio_temporal_aggregation,Show a heatmap of average PM2.5 for the top 8 most polluted states by month for 2017.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    top8 = data.groupby('state')['PM2.5'].mean().nlargest(8).index.tolist()
    df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2017)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_rect().encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('state:N', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
        tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Heatmap – Top 8 Polluted States by Month (2017)', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     top8 = data.groupby('state')['PM2.5'].mean().nlargest(8).index.tolist()
     df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2017)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_rect().encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('state:N', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
         tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Heatmap – Top 8 Polluted States by Month (2017)', width=500, height=300)
     return chart


</code>
"
1711,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Gujarat, Telangana, and Arunachal Pradesh in 2018 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Gujarat', 'Telangana', 'Arunachal Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2018)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Gujarat, Telangana, UP – 2018', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Gujarat', 'Telangana', 'Arunachal Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2018)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Gujarat, Telangana, UP – 2018', width=550, height=320)
     return chart


</code>
"
1712,spatio_temporal_aggregation,Show a heatmap of average PM2.5 for the top 6 most polluted states by month for 2018.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    top8 = data.groupby('state')['PM2.5'].mean().nlargest(6).index.tolist()
    df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2018)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_rect().encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('state:N', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
        tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Heatmap – Top 6 Polluted States by Month (2018)', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     top8 = data.groupby('state')['PM2.5'].mean().nlargest(6).index.tolist()
     df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2018)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_rect().encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('state:N', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
         tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Heatmap – Top 6 Polluted States by Month (2018)', width=500, height=300)
     return chart


</code>
"
1713,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Mizoram, Andhra Pradesh, and Telangana in 2017 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Mizoram', 'Andhra Pradesh', 'Telangana']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2017)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Mizoram, Andhra Pradesh, UP – 2017', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Mizoram', 'Andhra Pradesh', 'Telangana']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2017)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Mizoram, Andhra Pradesh, UP – 2017', width=550, height=320)
     return chart


</code>
"
1714,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Sikkim stations in 2018, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Sikkim') & (data['Timestamp'].dt.year == 2018)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Sikkim Stations 2018', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Sikkim') & (data['Timestamp'].dt.year == 2018)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Sikkim Stations 2018', width=450, height=350)


</code>
"
1715,specific_pattern,Plot the rolling 30-day average PM2.5 for Meghalaya in 2020 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Meghalaya') & (data['Timestamp'].dt.year == 2020)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Meghalaya 2020', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Meghalaya') & (data['Timestamp'].dt.year == 2020)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Meghalaya 2020', width=600, height=300)


</code>
"
1716,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Howrah, Kolkata, and Sri Ganganagar in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Howrah', 'Kolkata', 'Sri Ganganagar']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Howrah vs Kolkata vs Sri Ganganagar – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Howrah', 'Kolkata', 'Sri Ganganagar']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Howrah vs Kolkata vs Sri Ganganagar – 2022', width=550, height=320)
     return chart


</code>
"
1717,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Haryana, Manipur, and Assam across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Haryana', 'Manipur', 'Assam']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Haryana', 'Manipur', 'Assam']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
1718,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Karnataka, Telangana, and Chhattisgarh from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Karnataka', 'Telangana', 'Chhattisgarh']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Karnataka vs Telangana vs Chhattisgarh', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Karnataka', 'Telangana', 'Chhattisgarh']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Karnataka vs Telangana vs Chhattisgarh', width=550, height=320)
     return chart


</code>
"
1719,temporal_aggregation,Show the monthly average PM2.5 for Brajrajnagar in 2019 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Brajrajnagar') & (data['Timestamp'].dt.year == 2019)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Brajrajnagar 2019', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Brajrajnagar') & (data['Timestamp'].dt.year == 2019)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Brajrajnagar 2019', width=450, height=280)


</code>
"
1720,temporal_aggregation,Show the monthly average PM2.5 for Greater Noida in 2022 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Greater Noida') & (data['Timestamp'].dt.year == 2022)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Greater Noida 2022', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Greater Noida') & (data['Timestamp'].dt.year == 2022)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Greater Noida 2022', width=450, height=280)


</code>
"
1721,spatial_aggregation,Visualize the bottom 9 states with the lowest average PM2.5 in 2018 using a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2018]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nsmallest(9, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Bottom 9 States by Average PM2.5 in 2018', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2018]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nsmallest(9, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Bottom 9 States by Average PM2.5 in 2018', width=500, height=300)
     return chart


</code>
"
1722,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Manipur stations in 2023, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Manipur') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Manipur Stations 2023', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Manipur') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Manipur Stations 2023', width=450, height=350)


</code>
"
1723,spatial_aggregation,"Show the top 11 states by average PM10 in 2022 as a bar chart, sorted in descending order.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2022]
    df = df.groupby('state')['PM10'].mean().reset_index().dropna()
    df = df.nlargest(11, 'PM10')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Top 11 States by Average PM10 in 2022', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2022]
     df = df.groupby('state')['PM10'].mean().reset_index().dropna()
     df = df.nlargest(11, 'PM10')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Top 11 States by Average PM10 in 2022', width=500, height=300)
     return chart


</code>
"
1724,specific_pattern,Plot the rolling 30-day average PM2.5 for Assam in 2020 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Assam') & (data['Timestamp'].dt.year == 2020)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Assam 2020', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Assam') & (data['Timestamp'].dt.year == 2020)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Assam 2020', width=600, height=300)


</code>
"
1725,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Uttar Pradesh, Tamil Nadu, and Delhi from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Uttar Pradesh', 'Tamil Nadu', 'Delhi']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Uttar Pradesh vs Tamil Nadu vs Delhi', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Uttar Pradesh', 'Tamil Nadu', 'Delhi']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Uttar Pradesh vs Tamil Nadu vs Delhi', width=550, height=320)
     return chart


</code>
"
1726,temporal_aggregation,Show a monthly bar chart of the number of days Manipur exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2022.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Manipur') & (data['Timestamp'].dt.year == 2022)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Manipur Exceeded WHO PM2.5 Guideline per Month – 2022', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Manipur') & (data['Timestamp'].dt.year == 2022)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Manipur Exceeded WHO PM2.5 Guideline per Month – 2022', width=500, height=300)
     return chart


</code>
"
1727,temporal_aggregation,Show the monthly average PM10 trend for Keonjhar from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Keonjhar'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Keonjhar (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Keonjhar'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Keonjhar (2019–2024)', width=600, height=300)
     return chart


</code>
"
1728,temporal_aggregation,Plot the weekly average PM2.5 for Bengaluru in 2021 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Bengaluru') & (data['Timestamp'].dt.year == 2021)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Bengaluru 2021', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Bengaluru') & (data['Timestamp'].dt.year == 2021)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Bengaluru 2021', width=600, height=300)
     return chart


</code>
"
1729,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Chittoor, Rajgir, and Jind in 2020 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Chittoor', 'Rajgir', 'Jind']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2020)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Chittoor vs Rajgir vs Jind – 2020', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Chittoor', 'Rajgir', 'Jind']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2020)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Chittoor vs Rajgir vs Jind – 2020', width=550, height=320)
     return chart


</code>
"
1730,spatio_temporal_aggregation,Show a heatmap of average PM2.5 for the top 15 most polluted states by month for 2017.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    top8 = data.groupby('state')['PM2.5'].mean().nlargest(15).index.tolist()
    df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2017)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_rect().encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('state:N', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
        tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Heatmap – Top 15 Polluted States by Month (2017)', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     top8 = data.groupby('state')['PM2.5'].mean().nlargest(15).index.tolist()
     df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2017)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_rect().encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('state:N', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
         tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Heatmap – Top 15 Polluted States by Month (2017)', width=500, height=300)
     return chart


</code>
"
1731,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Jalgaon, Barbil, and Badlapur in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Jalgaon', 'Barbil', 'Badlapur']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Jalgaon vs Barbil vs Badlapur – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Jalgaon', 'Barbil', 'Badlapur']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Jalgaon vs Barbil vs Badlapur – 2023', width=550, height=320)
     return chart


</code>
"
1732,specific_pattern,Show a cumulative area chart of PM2.5 readings for Vrindavan across 2024.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Vrindavan') & (data['Timestamp'].dt.year == 2024)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Vrindavan 2024', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Vrindavan') & (data['Timestamp'].dt.year == 2024)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Vrindavan 2024', width=600, height=300)
     return chart


</code>
"
1733,spatial_aggregation,Plot the top 6 states by average PM2.5 in 2018 as a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2018]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nlargest(6, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 6 States by Average PM2.5 in 2018', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2018]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nlargest(6, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 6 States by Average PM2.5 in 2018', width=500, height=300)
     return chart


</code>
"
1734,temporal_aggregation,Show a monthly bar chart of the number of days Karnataka exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2022.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Karnataka') & (data['Timestamp'].dt.year == 2022)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Karnataka Exceeded WHO PM2.5 Guideline per Month – 2022', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Karnataka') & (data['Timestamp'].dt.year == 2022)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Karnataka Exceeded WHO PM2.5 Guideline per Month – 2022', width=500, height=300)
     return chart


</code>
"
1735,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Jammu and Kashmir, Assam, and Jammu and Kashmir across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Jammu and Kashmir', 'Assam', 'Jammu and Kashmir']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Jammu and Kashmir', 'Assam', 'Jammu and Kashmir']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
1736,temporal_aggregation,Plot the weekly average PM2.5 for Sawai Madhopur in 2023 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Sawai Madhopur') & (data['Timestamp'].dt.year == 2023)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Sawai Madhopur 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Sawai Madhopur') & (data['Timestamp'].dt.year == 2023)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Sawai Madhopur 2023', width=600, height=300)
     return chart


</code>
"
1737,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Haryana, Chhattisgarh, and Odisha across 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Haryana', 'Chhattisgarh', 'Odisha']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Haryana', 'Chhattisgarh', 'Odisha']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
     return chart


</code>
"
1738,spatial_aggregation,Show a bar chart of the top 11 cities by median PM2.5 in 2019.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2019]
    df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
    df = df.nlargest(11, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
        y=alt.Y('city:N', sort='-x', title='City'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
        tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 11 Cities by Median PM2.5 in 2019', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2019]
     df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
     df = df.nlargest(11, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
         y=alt.Y('city:N', sort='-x', title='City'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
         tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 11 Cities by Median PM2.5 in 2019', width=500, height=300)
     return chart


</code>
"
1739,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Maharashtra stations in 2020, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Maharashtra') & (data['Timestamp'].dt.year == 2020)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Maharashtra Stations 2020', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Maharashtra') & (data['Timestamp'].dt.year == 2020)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Maharashtra Stations 2020', width=450, height=350)


</code>
"
1740,specific_pattern,Show a cumulative area chart of PM2.5 readings for Navi Mumbai across 2022.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Navi Mumbai') & (data['Timestamp'].dt.year == 2022)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Navi Mumbai 2022', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Navi Mumbai') & (data['Timestamp'].dt.year == 2022)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Navi Mumbai 2022', width=600, height=300)
     return chart


</code>
"
1741,temporal_aggregation,Show the monthly average PM2.5 for Coimbatore in 2017 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Coimbatore') & (data['Timestamp'].dt.year == 2017)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Coimbatore 2017', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Coimbatore') & (data['Timestamp'].dt.year == 2017)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Coimbatore 2017', width=450, height=280)


</code>
"
1742,temporal_aggregation,Plot the weekly average PM2.5 for Firozabad in 2023 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Firozabad') & (data['Timestamp'].dt.year == 2023)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Firozabad 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Firozabad') & (data['Timestamp'].dt.year == 2023)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Firozabad 2023', width=600, height=300)
     return chart


</code>
"
1743,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Meghalaya stations in 2023, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Meghalaya') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Meghalaya Stations 2023', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Meghalaya') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Meghalaya Stations 2023', width=450, height=350)


</code>
"
1744,temporal_aggregation,Show the monthly average PM2.5 for Alwar in 2023 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Alwar') & (data['Timestamp'].dt.year == 2023)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Alwar 2023', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Alwar') & (data['Timestamp'].dt.year == 2023)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Alwar 2023', width=450, height=280)


</code>
"
1745,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Jharkhand stations in 2019, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Jharkhand') & (data['Timestamp'].dt.year == 2019)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Jharkhand Stations 2019', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Jharkhand') & (data['Timestamp'].dt.year == 2019)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Jharkhand Stations 2019', width=450, height=350)


</code>
"
1746,specific_pattern,Plot the rolling 30-day average PM2.5 for Odisha in 2020 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Odisha') & (data['Timestamp'].dt.year == 2020)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Odisha 2020', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Odisha') & (data['Timestamp'].dt.year == 2020)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Odisha 2020', width=600, height=300)


</code>
"
1747,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Gujarat stations in 2018, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Gujarat') & (data['Timestamp'].dt.year == 2018)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Gujarat Stations 2018', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Gujarat') & (data['Timestamp'].dt.year == 2018)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Gujarat Stations 2018', width=450, height=350)


</code>
"
1748,temporal_aggregation,Show the monthly average PM2.5 for Sangli in 2024 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Sangli') & (data['Timestamp'].dt.year == 2024)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Sangli 2024', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Sangli') & (data['Timestamp'].dt.year == 2024)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Sangli 2024', width=450, height=280)


</code>
"
1749,spatio_temporal_aggregation,Show a heatmap of average PM2.5 for the top 8 most polluted states by month for 2024.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    top8 = data.groupby('state')['PM2.5'].mean().nlargest(8).index.tolist()
    df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_rect().encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('state:N', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
        tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Heatmap – Top 8 Polluted States by Month (2024)', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     top8 = data.groupby('state')['PM2.5'].mean().nlargest(8).index.tolist()
     df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_rect().encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('state:N', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
         tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Heatmap – Top 8 Polluted States by Month (2024)', width=500, height=300)
     return chart


</code>
"
1750,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Ahmedabad, Jaisalmer, and Patna in 2019 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Ahmedabad', 'Jaisalmer', 'Patna']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Ahmedabad vs Jaisalmer vs Patna – 2019', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Ahmedabad', 'Jaisalmer', 'Patna']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Ahmedabad vs Jaisalmer vs Patna – 2019', width=550, height=320)
     return chart


</code>
"
1751,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Andhra Pradesh stations in 2017, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Andhra Pradesh') & (data['Timestamp'].dt.year == 2017)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Andhra Pradesh Stations 2017', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Andhra Pradesh') & (data['Timestamp'].dt.year == 2017)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Andhra Pradesh Stations 2017', width=450, height=350)


</code>
"
1752,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Delhi, Karnataka, and Nagaland in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Delhi', 'Karnataka', 'Nagaland']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Delhi, Karnataka, UP – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Delhi', 'Karnataka', 'Nagaland']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Delhi, Karnataka, UP – 2022', width=550, height=320)
     return chart


</code>
"
1753,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Bihar, Tamil Nadu, and Punjab across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Bihar', 'Tamil Nadu', 'Punjab']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Bihar', 'Tamil Nadu', 'Punjab']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
1754,temporal_aggregation,Show the monthly average PM10 trend for Nayagarh from 2017 to 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Nayagarh'].copy()
    df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Nayagarh (2017–2022)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Nayagarh'].copy()
     df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Nayagarh (2017–2022)', width=600, height=300)
     return chart


</code>
"
1755,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Latur, Dehradun, and Vijayawada in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Latur', 'Dehradun', 'Vijayawada']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Latur vs Dehradun vs Vijayawada – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Latur', 'Dehradun', 'Vijayawada']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Latur vs Dehradun vs Vijayawada – 2024', width=550, height=320)
     return chart


</code>
"
1756,temporal_aggregation,Show the monthly average PM10 trend for Motihari from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Motihari'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Motihari (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Motihari'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Motihari (2019–2024)', width=600, height=300)
     return chart


</code>
"
1757,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Gujarat, Himachal Pradesh, and Himachal Pradesh in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Gujarat', 'Himachal Pradesh', 'Himachal Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Gujarat, Himachal Pradesh, UP – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Gujarat', 'Himachal Pradesh', 'Himachal Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Gujarat, Himachal Pradesh, UP – 2023', width=550, height=320)
     return chart


</code>
"
1758,temporal_aggregation,Show the monthly average PM2.5 for Jalgaon in 2022 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Jalgaon') & (data['Timestamp'].dt.year == 2022)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Jalgaon 2022', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Jalgaon') & (data['Timestamp'].dt.year == 2022)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Jalgaon 2022', width=450, height=280)


</code>
"
1759,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Bhilai, Kanpur, and Ballabgarh in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Bhilai', 'Kanpur', 'Ballabgarh']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Bhilai vs Kanpur vs Ballabgarh – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Bhilai', 'Kanpur', 'Ballabgarh']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Bhilai vs Kanpur vs Ballabgarh – 2023', width=550, height=320)
     return chart


</code>
"
1760,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Udaipur, Araria, and Gandhinagar in 2017 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Udaipur', 'Araria', 'Gandhinagar']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2017)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Udaipur vs Araria vs Gandhinagar – 2017', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Udaipur', 'Araria', 'Gandhinagar']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2017)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Udaipur vs Araria vs Gandhinagar – 2017', width=550, height=320)
     return chart


</code>
"
1761,temporal_aggregation,Plot the weekly average PM2.5 for Muzaffarnagar in 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Muzaffarnagar') & (data['Timestamp'].dt.year == 2024)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Muzaffarnagar 2024', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Muzaffarnagar') & (data['Timestamp'].dt.year == 2024)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Muzaffarnagar 2024', width=600, height=300)
     return chart


</code>
"
1762,temporal_aggregation,Plot the weekly average PM2.5 for Ludhiana in 2017 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Ludhiana') & (data['Timestamp'].dt.year == 2017)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Ludhiana 2017', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Ludhiana') & (data['Timestamp'].dt.year == 2017)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Ludhiana 2017', width=600, height=300)
     return chart


</code>
"
1763,temporal_aggregation,Show the monthly average PM10 trend for Bhilai from 2017 to 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Bhilai'].copy()
    df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Bhilai (2017–2022)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Bhilai'].copy()
     df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Bhilai (2017–2022)', width=600, height=300)
     return chart


</code>
"
1764,temporal_aggregation,Show the monthly average PM2.5 for Manguraha in 2017 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Manguraha') & (data['Timestamp'].dt.year == 2017)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Manguraha 2017', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Manguraha') & (data['Timestamp'].dt.year == 2017)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Manguraha 2017', width=450, height=280)


</code>
"
1765,temporal_aggregation,Show the monthly average PM2.5 for Dhule in 2019 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Dhule') & (data['Timestamp'].dt.year == 2019)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Dhule 2019', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Dhule') & (data['Timestamp'].dt.year == 2019)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Dhule 2019', width=450, height=280)


</code>
"
1766,temporal_aggregation,Show the monthly average PM2.5 for Karur in 2023 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Karur') & (data['Timestamp'].dt.year == 2023)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Karur 2023', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Karur') & (data['Timestamp'].dt.year == 2023)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Karur 2023', width=450, height=280)


</code>
"
1767,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Mizoram, Uttar Pradesh, and Jharkhand across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Mizoram', 'Uttar Pradesh', 'Jharkhand']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Mizoram', 'Uttar Pradesh', 'Jharkhand']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
1768,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Puducherry, Delhi, and Jharkhand in 2017 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Puducherry', 'Delhi', 'Jharkhand']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2017)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Puducherry, Delhi, UP – 2017', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Puducherry', 'Delhi', 'Jharkhand']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2017)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Puducherry, Delhi, UP – 2017', width=550, height=320)
     return chart


</code>
"
1769,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Meghalaya, Karnataka, and Assam from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Meghalaya', 'Karnataka', 'Assam']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Meghalaya vs Karnataka vs Assam', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Meghalaya', 'Karnataka', 'Assam']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Meghalaya vs Karnataka vs Assam', width=550, height=320)
     return chart


</code>
"
1770,temporal_aggregation,Plot the weekly average PM2.5 for Hapur in 2021 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Hapur') & (data['Timestamp'].dt.year == 2021)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Hapur 2021', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Hapur') & (data['Timestamp'].dt.year == 2021)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Hapur 2021', width=600, height=300)
     return chart


</code>
"
1771,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Sikkim stations in 2023, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Sikkim') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Sikkim Stations 2023', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Sikkim') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Sikkim Stations 2023', width=450, height=350)


</code>
"
1772,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Telangana, Delhi, and Jammu and Kashmir across 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Telangana', 'Delhi', 'Jammu and Kashmir']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Telangana', 'Delhi', 'Jammu and Kashmir']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
     return chart


</code>
"
1773,temporal_aggregation,Show the monthly average PM2.5 for Sivasagar in 2017 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Sivasagar') & (data['Timestamp'].dt.year == 2017)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Sivasagar 2017', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Sivasagar') & (data['Timestamp'].dt.year == 2017)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Sivasagar 2017', width=450, height=280)


</code>
"
1774,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Himachal Pradesh, Andhra Pradesh, and Odisha in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Himachal Pradesh', 'Andhra Pradesh', 'Odisha']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Himachal Pradesh, Andhra Pradesh, UP – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Himachal Pradesh', 'Andhra Pradesh', 'Odisha']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Himachal Pradesh, Andhra Pradesh, UP – 2022', width=550, height=320)
     return chart


</code>
"
1775,temporal_aggregation,Show the monthly average PM10 trend for Asansol from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Asansol'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Asansol (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Asansol'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Asansol (2019–2024)', width=600, height=300)
     return chart


</code>
"
1776,specific_pattern,Show a cumulative area chart of PM2.5 readings for Raipur across 2023.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Raipur') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Raipur 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Raipur') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Raipur 2023', width=600, height=300)
     return chart


</code>
"
1777,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Chhattisgarh, Delhi, and Assam from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Chhattisgarh', 'Delhi', 'Assam']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Chhattisgarh vs Delhi vs Assam', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Chhattisgarh', 'Delhi', 'Assam']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Chhattisgarh vs Delhi vs Assam', width=550, height=320)
     return chart


</code>
"
1778,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Bahadurgarh, Bulandshahr, and Bagalkot in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Bahadurgarh', 'Bulandshahr', 'Bagalkot']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Bahadurgarh vs Bulandshahr vs Bagalkot – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Bahadurgarh', 'Bulandshahr', 'Bagalkot']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Bahadurgarh vs Bulandshahr vs Bagalkot – 2024', width=550, height=320)
     return chart


</code>
"
1779,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Madhya Pradesh stations in 2023, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Madhya Pradesh') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Madhya Pradesh Stations 2023', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Madhya Pradesh') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Madhya Pradesh Stations 2023', width=450, height=350)


</code>
"
1780,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Puducherry, Maharashtra, and Haryana in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Puducherry', 'Maharashtra', 'Haryana']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Puducherry, Maharashtra, UP – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Puducherry', 'Maharashtra', 'Haryana']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Puducherry, Maharashtra, UP – 2022', width=550, height=320)
     return chart


</code>
"
1781,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Rajasthan, Odisha, and Tripura in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Rajasthan', 'Odisha', 'Tripura']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Rajasthan, Odisha, UP – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Rajasthan', 'Odisha', 'Tripura']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Rajasthan, Odisha, UP – 2023', width=550, height=320)
     return chart


</code>
"
1782,temporal_aggregation,Show the monthly average PM2.5 for Bileipada in 2020 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Bileipada') & (data['Timestamp'].dt.year == 2020)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Bileipada 2020', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Bileipada') & (data['Timestamp'].dt.year == 2020)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Bileipada 2020', width=450, height=280)


</code>
"
1783,temporal_aggregation,Show the monthly average PM10 trend for Muzaffarnagar from 2017 to 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Muzaffarnagar'].copy()
    df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Muzaffarnagar (2017–2022)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Muzaffarnagar'].copy()
     df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Muzaffarnagar (2017–2022)', width=600, height=300)
     return chart


</code>
"
1784,temporal_aggregation,Show the monthly average PM2.5 for Palkalaiperur in 2024 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Palkalaiperur') & (data['Timestamp'].dt.year == 2024)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Palkalaiperur 2024', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Palkalaiperur') & (data['Timestamp'].dt.year == 2024)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Palkalaiperur 2024', width=450, height=280)


</code>
"
1785,temporal_aggregation,Show the monthly average PM2.5 for Karnal in 2017 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Karnal') & (data['Timestamp'].dt.year == 2017)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Karnal 2017', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Karnal') & (data['Timestamp'].dt.year == 2017)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Karnal 2017', width=450, height=280)


</code>
"
1786,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Puducherry, West Bengal, and Himachal Pradesh across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Puducherry', 'West Bengal', 'Himachal Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Puducherry', 'West Bengal', 'Himachal Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
1787,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Chandigarh, Manipur, and Karnataka across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Chandigarh', 'Manipur', 'Karnataka']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Chandigarh', 'Manipur', 'Karnataka']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
1788,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Arunachal Pradesh, Haryana, and Madhya Pradesh across 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Arunachal Pradesh', 'Haryana', 'Madhya Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Arunachal Pradesh', 'Haryana', 'Madhya Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
     return chart


</code>
"
1789,spatio_temporal_aggregation,Show a heatmap of average PM2.5 for the top 5 most polluted states by month for 2017.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    top8 = data.groupby('state')['PM2.5'].mean().nlargest(5).index.tolist()
    df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2017)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_rect().encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('state:N', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
        tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Heatmap – Top 5 Polluted States by Month (2017)', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     top8 = data.groupby('state')['PM2.5'].mean().nlargest(5).index.tolist()
     df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2017)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_rect().encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('state:N', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
         tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Heatmap – Top 5 Polluted States by Month (2017)', width=500, height=300)
     return chart


</code>
"
1790,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Himachal Pradesh, Karnataka, and Delhi across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Himachal Pradesh', 'Karnataka', 'Delhi']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Himachal Pradesh', 'Karnataka', 'Delhi']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
1791,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Puducherry, Odisha, and Arunachal Pradesh across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Puducherry', 'Odisha', 'Arunachal Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Puducherry', 'Odisha', 'Arunachal Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
1792,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Bihar, Meghalaya, and Haryana in 2017 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Bihar', 'Meghalaya', 'Haryana']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2017)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Bihar, Meghalaya, UP – 2017', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Bihar', 'Meghalaya', 'Haryana']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2017)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Bihar, Meghalaya, UP – 2017', width=550, height=320)
     return chart


</code>
"
1793,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Uttar Pradesh, Punjab, and Nagaland from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Uttar Pradesh', 'Punjab', 'Nagaland']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Uttar Pradesh vs Punjab vs Nagaland', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Uttar Pradesh', 'Punjab', 'Nagaland']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Uttar Pradesh vs Punjab vs Nagaland', width=550, height=320)
     return chart


</code>
"
1794,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Rajasthan, Uttar Pradesh, and Telangana from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Rajasthan', 'Uttar Pradesh', 'Telangana']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Rajasthan vs Uttar Pradesh vs Telangana', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Rajasthan', 'Uttar Pradesh', 'Telangana']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Rajasthan vs Uttar Pradesh vs Telangana', width=550, height=320)
     return chart


</code>
"
1795,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Andhra Pradesh, Haryana, and Chhattisgarh across 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Andhra Pradesh', 'Haryana', 'Chhattisgarh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Andhra Pradesh', 'Haryana', 'Chhattisgarh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
     return chart


</code>
"
1796,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Manipur, Sikkim, and Chhattisgarh from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Manipur', 'Sikkim', 'Chhattisgarh']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Manipur vs Sikkim vs Chhattisgarh', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Manipur', 'Sikkim', 'Chhattisgarh']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Manipur vs Sikkim vs Chhattisgarh', width=550, height=320)
     return chart


</code>
"
1797,spatio_temporal_aggregation,Show a heatmap of average PM2.5 for the top 5 most polluted states by month for 2021.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    top8 = data.groupby('state')['PM2.5'].mean().nlargest(5).index.tolist()
    df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2021)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_rect().encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('state:N', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
        tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Heatmap – Top 5 Polluted States by Month (2021)', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     top8 = data.groupby('state')['PM2.5'].mean().nlargest(5).index.tolist()
     df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2021)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_rect().encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('state:N', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
         tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Heatmap – Top 5 Polluted States by Month (2021)', width=500, height=300)
     return chart


</code>
"
1798,specific_pattern,Plot the rolling 30-day average PM2.5 for Rajasthan in 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Rajasthan') & (data['Timestamp'].dt.year == 2022)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Rajasthan 2022', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Rajasthan') & (data['Timestamp'].dt.year == 2022)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Rajasthan 2022', width=600, height=300)


</code>
"
1799,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Gujarat stations in 2024, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Gujarat') & (data['Timestamp'].dt.year == 2024)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Gujarat Stations 2024', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Gujarat') & (data['Timestamp'].dt.year == 2024)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Gujarat Stations 2024', width=450, height=350)


</code>
"
1800,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Punjab, Rajasthan, and Kerala in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Punjab', 'Rajasthan', 'Kerala']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Punjab, Rajasthan, UP – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Punjab', 'Rajasthan', 'Kerala']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Punjab, Rajasthan, UP – 2022', width=550, height=320)
     return chart


</code>
"
1801,temporal_aggregation,Show the monthly average PM2.5 for Brajrajnagar in 2020 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Brajrajnagar') & (data['Timestamp'].dt.year == 2020)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Brajrajnagar 2020', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Brajrajnagar') & (data['Timestamp'].dt.year == 2020)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Brajrajnagar 2020', width=450, height=280)


</code>
"
1802,specific_pattern,Plot the rolling 30-day average PM2.5 for Uttar Pradesh in 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Uttar Pradesh') & (data['Timestamp'].dt.year == 2022)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Uttar Pradesh 2022', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Uttar Pradesh') & (data['Timestamp'].dt.year == 2022)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Uttar Pradesh 2022', width=600, height=300)


</code>
"
1803,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Nayagarh, Madurai, and Manguraha in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Nayagarh', 'Madurai', 'Manguraha']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Nayagarh vs Madurai vs Manguraha – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Nayagarh', 'Madurai', 'Manguraha']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Nayagarh vs Madurai vs Manguraha – 2023', width=550, height=320)
     return chart


</code>
"
1804,temporal_aggregation,Show the monthly average PM2.5 for Thrissur in 2017 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Thrissur') & (data['Timestamp'].dt.year == 2017)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Thrissur 2017', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Thrissur') & (data['Timestamp'].dt.year == 2017)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Thrissur 2017', width=450, height=280)


</code>
"
1805,specific_pattern,Show a cumulative area chart of PM2.5 readings for Chandigarh across 2019.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Chandigarh') & (data['Timestamp'].dt.year == 2019)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Chandigarh 2019', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Chandigarh') & (data['Timestamp'].dt.year == 2019)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Chandigarh 2019', width=600, height=300)
     return chart


</code>
"
1806,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Mizoram, Tamil Nadu, and West Bengal across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Mizoram', 'Tamil Nadu', 'West Bengal']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Mizoram', 'Tamil Nadu', 'West Bengal']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
1807,spatial_aggregation,Plot the top 5 states by average PM2.5 in 2021 as a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2021]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nlargest(5, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 5 States by Average PM2.5 in 2021', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2021]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nlargest(5, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 5 States by Average PM2.5 in 2021', width=500, height=300)
     return chart


</code>
"
1808,temporal_aggregation,Plot the weekly average PM2.5 for Chandrapur in 2019 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Chandrapur') & (data['Timestamp'].dt.year == 2019)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Chandrapur 2019', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Chandrapur') & (data['Timestamp'].dt.year == 2019)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Chandrapur 2019', width=600, height=300)
     return chart


</code>
"
1809,temporal_aggregation,Show the monthly average PM10 trend for Kashipur from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Kashipur'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Kashipur (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Kashipur'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Kashipur (2019–2024)', width=600, height=300)
     return chart


</code>
"
1810,temporal_aggregation,Show the monthly average PM2.5 for Bharatpur in 2020 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Bharatpur') & (data['Timestamp'].dt.year == 2020)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Bharatpur 2020', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Bharatpur') & (data['Timestamp'].dt.year == 2020)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Bharatpur 2020', width=450, height=280)


</code>
"
1811,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Odisha, Puducherry, and Karnataka in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Odisha', 'Puducherry', 'Karnataka']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Odisha, Puducherry, UP – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Odisha', 'Puducherry', 'Karnataka']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Odisha, Puducherry, UP – 2024', width=550, height=320)
     return chart


</code>
"
1812,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for West Bengal, Bihar, and Uttar Pradesh across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['West Bengal', 'Bihar', 'Uttar Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['West Bengal', 'Bihar', 'Uttar Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
1813,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Yadgir, Dhule, and Korba in 2019 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Yadgir', 'Dhule', 'Korba']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Yadgir vs Dhule vs Korba – 2019', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Yadgir', 'Dhule', 'Korba']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Yadgir vs Dhule vs Korba – 2019', width=550, height=320)
     return chart


</code>
"
1814,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Tamil Nadu, Chandigarh, and Mizoram across 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Tamil Nadu', 'Chandigarh', 'Mizoram']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Tamil Nadu', 'Chandigarh', 'Mizoram']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
     return chart


</code>
"
1815,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Gorakhpur, Vijayawada, and Perundurai in 2018 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Gorakhpur', 'Vijayawada', 'Perundurai']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2018)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Gorakhpur vs Vijayawada vs Perundurai – 2018', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Gorakhpur', 'Vijayawada', 'Perundurai']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2018)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Gorakhpur vs Vijayawada vs Perundurai – 2018', width=550, height=320)
     return chart


</code>
"
1816,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Chandigarh, Madhya Pradesh, and Manipur across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Chandigarh', 'Madhya Pradesh', 'Manipur']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Chandigarh', 'Madhya Pradesh', 'Manipur']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
1817,specific_pattern,Plot the rolling 30-day average PM2.5 for Jharkhand in 2023 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Jharkhand') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Jharkhand 2023', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Jharkhand') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Jharkhand 2023', width=600, height=300)


</code>
"
1818,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Kerala, Madhya Pradesh, and West Bengal across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Kerala', 'Madhya Pradesh', 'West Bengal']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Kerala', 'Madhya Pradesh', 'West Bengal']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
1819,spatial_aggregation,Plot the top 13 states by average PM2.5 in 2021 as a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2021]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nlargest(13, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 13 States by Average PM2.5 in 2021', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2021]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nlargest(13, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 13 States by Average PM2.5 in 2021', width=500, height=300)
     return chart


</code>
"
1820,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Uttar Pradesh, Odisha, and Puducherry from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Uttar Pradesh', 'Odisha', 'Puducherry']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Uttar Pradesh vs Odisha vs Puducherry', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Uttar Pradesh', 'Odisha', 'Puducherry']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Uttar Pradesh vs Odisha vs Puducherry', width=550, height=320)
     return chart


</code>
"
1821,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Maharashtra, Bihar, and Andhra Pradesh in 2018 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Maharashtra', 'Bihar', 'Andhra Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2018)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Maharashtra, Bihar, UP – 2018', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Maharashtra', 'Bihar', 'Andhra Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2018)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Maharashtra, Bihar, UP – 2018', width=550, height=320)
     return chart


</code>
"
1822,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Meghalaya, Tripura, and Haryana in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Meghalaya', 'Tripura', 'Haryana']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Meghalaya, Tripura, UP – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Meghalaya', 'Tripura', 'Haryana']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Meghalaya, Tripura, UP – 2024', width=550, height=320)
     return chart


</code>
"
1823,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Odisha, Chhattisgarh, and Andhra Pradesh across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Odisha', 'Chhattisgarh', 'Andhra Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Odisha', 'Chhattisgarh', 'Andhra Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
1824,specific_pattern,Show a cumulative area chart of PM2.5 readings for Gorakhpur across 2022.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Gorakhpur') & (data['Timestamp'].dt.year == 2022)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Gorakhpur 2022', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Gorakhpur') & (data['Timestamp'].dt.year == 2022)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Gorakhpur 2022', width=600, height=300)
     return chart


</code>
"
1825,specific_pattern,Show a cumulative area chart of PM2.5 readings for Boisar across 2024.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Boisar') & (data['Timestamp'].dt.year == 2024)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Boisar 2024', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Boisar') & (data['Timestamp'].dt.year == 2024)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Boisar 2024', width=600, height=300)
     return chart


</code>
"
1826,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Andhra Pradesh, Madhya Pradesh, and Himachal Pradesh across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Andhra Pradesh', 'Madhya Pradesh', 'Himachal Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Andhra Pradesh', 'Madhya Pradesh', 'Himachal Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
1827,spatial_aggregation,Show a bar chart of the top 5 cities by median PM2.5 in 2017.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2017]
    df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
    df = df.nlargest(5, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
        y=alt.Y('city:N', sort='-x', title='City'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
        tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 5 Cities by Median PM2.5 in 2017', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2017]
     df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
     df = df.nlargest(5, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
         y=alt.Y('city:N', sort='-x', title='City'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
         tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 5 Cities by Median PM2.5 in 2017', width=500, height=300)
     return chart


</code>
"
1828,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Sikkim, Uttarakhand, and Tamil Nadu from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Sikkim', 'Uttarakhand', 'Tamil Nadu']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Sikkim vs Uttarakhand vs Tamil Nadu', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Sikkim', 'Uttarakhand', 'Tamil Nadu']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Sikkim vs Uttarakhand vs Tamil Nadu', width=550, height=320)
     return chart


</code>
"
1829,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Chhattisgarh, Rajasthan, and Rajasthan in 2017 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Chhattisgarh', 'Rajasthan', 'Rajasthan']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2017)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Chhattisgarh, Rajasthan, UP – 2017', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Chhattisgarh', 'Rajasthan', 'Rajasthan']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2017)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Chhattisgarh, Rajasthan, UP – 2017', width=550, height=320)
     return chart


</code>
"
1830,temporal_aggregation,Show the monthly average PM10 trend for Gaya from 2017 to 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Gaya'].copy()
    df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Gaya (2017–2022)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Gaya'].copy()
     df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Gaya (2017–2022)', width=600, height=300)
     return chart


</code>
"
1831,temporal_aggregation,Show the monthly average PM2.5 for Delhi in 2023 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Delhi') & (data['Timestamp'].dt.year == 2023)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Delhi 2023', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Delhi') & (data['Timestamp'].dt.year == 2023)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Delhi 2023', width=450, height=280)


</code>
"
1832,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Assam, Andhra Pradesh, and Mizoram across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Assam', 'Andhra Pradesh', 'Mizoram']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Assam', 'Andhra Pradesh', 'Mizoram']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
1833,temporal_aggregation,Show the monthly average PM2.5 for Guwahati in 2024 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Guwahati') & (data['Timestamp'].dt.year == 2024)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Guwahati 2024', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Guwahati') & (data['Timestamp'].dt.year == 2024)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Guwahati 2024', width=450, height=280)


</code>
"
1834,temporal_aggregation,Show the monthly average PM10 trend for Rairangpur from 2017 to 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Rairangpur'].copy()
    df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Rairangpur (2017–2022)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Rairangpur'].copy()
     df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Rairangpur (2017–2022)', width=600, height=300)
     return chart


</code>
"
1835,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Tripura, Mizoram, and Assam across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Tripura', 'Mizoram', 'Assam']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Tripura', 'Mizoram', 'Assam']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
1836,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Telangana, Himachal Pradesh, and West Bengal from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Telangana', 'Himachal Pradesh', 'West Bengal']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Telangana vs Himachal Pradesh vs West Bengal', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Telangana', 'Himachal Pradesh', 'West Bengal']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Telangana vs Himachal Pradesh vs West Bengal', width=550, height=320)
     return chart


</code>
"
1837,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Davanagere, Bathinda, and Motihari in 2020 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Davanagere', 'Bathinda', 'Motihari']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2020)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Davanagere vs Bathinda vs Motihari – 2020', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Davanagere', 'Bathinda', 'Motihari']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2020)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Davanagere vs Bathinda vs Motihari – 2020', width=550, height=320)
     return chart


</code>
"
1838,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Mandi Gobindgarh, Ariyalur, and Araria in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Mandi Gobindgarh', 'Ariyalur', 'Araria']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Mandi Gobindgarh vs Ariyalur vs Araria – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Mandi Gobindgarh', 'Ariyalur', 'Araria']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Mandi Gobindgarh vs Ariyalur vs Araria – 2023', width=550, height=320)
     return chart


</code>
"
1839,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Delhi stations in 2019, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Delhi') & (data['Timestamp'].dt.year == 2019)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Delhi Stations 2019', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Delhi') & (data['Timestamp'].dt.year == 2019)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Delhi Stations 2019', width=450, height=350)


</code>
"
1840,temporal_aggregation,Plot the weekly average PM2.5 for Madikeri in 2021 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Madikeri') & (data['Timestamp'].dt.year == 2021)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Madikeri 2021', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Madikeri') & (data['Timestamp'].dt.year == 2021)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Madikeri 2021', width=600, height=300)
     return chart


</code>
"
1841,specific_pattern,Show a cumulative area chart of PM2.5 readings for Asansol across 2024.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Asansol') & (data['Timestamp'].dt.year == 2024)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Asansol 2024', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Asansol') & (data['Timestamp'].dt.year == 2024)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Asansol 2024', width=600, height=300)
     return chart


</code>
"
1842,temporal_aggregation,Show the monthly average PM10 trend for Badlapur from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Badlapur'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Badlapur (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Badlapur'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Badlapur (2019–2024)', width=600, height=300)
     return chart


</code>
"
1843,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Odisha, Kerala, and Jharkhand from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Odisha', 'Kerala', 'Jharkhand']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Odisha vs Kerala vs Jharkhand', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Odisha', 'Kerala', 'Jharkhand']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Odisha vs Kerala vs Jharkhand', width=550, height=320)
     return chart


</code>
"
1844,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Andhra Pradesh, Arunachal Pradesh, and Uttarakhand from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Andhra Pradesh', 'Arunachal Pradesh', 'Uttarakhand']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Andhra Pradesh vs Arunachal Pradesh vs Uttarakhand', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Andhra Pradesh', 'Arunachal Pradesh', 'Uttarakhand']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Andhra Pradesh vs Arunachal Pradesh vs Uttarakhand', width=550, height=320)
     return chart


</code>
"
1845,spatial_aggregation,"Show the top 12 states by average PM10 in 2022 as a bar chart, sorted in descending order.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2022]
    df = df.groupby('state')['PM10'].mean().reset_index().dropna()
    df = df.nlargest(12, 'PM10')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Top 12 States by Average PM10 in 2022', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2022]
     df = df.groupby('state')['PM10'].mean().reset_index().dropna()
     df = df.nlargest(12, 'PM10')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Top 12 States by Average PM10 in 2022', width=500, height=300)
     return chart


</code>
"
1846,temporal_aggregation,Show the monthly average PM10 trend for Puducherry from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Puducherry'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Puducherry (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Puducherry'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Puducherry (2019–2024)', width=600, height=300)
     return chart


</code>
"
1847,temporal_aggregation,Show the monthly average PM2.5 for Raipur in 2019 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Raipur') & (data['Timestamp'].dt.year == 2019)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Raipur 2019', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Raipur') & (data['Timestamp'].dt.year == 2019)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Raipur 2019', width=450, height=280)


</code>
"
1848,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Assam, Andhra Pradesh, and Mizoram from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Assam', 'Andhra Pradesh', 'Mizoram']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Assam vs Andhra Pradesh vs Mizoram', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Assam', 'Andhra Pradesh', 'Mizoram']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Assam vs Andhra Pradesh vs Mizoram', width=550, height=320)
     return chart


</code>
"
1849,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Rajasthan, Sikkim, and Rajasthan from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Rajasthan', 'Sikkim', 'Rajasthan']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Rajasthan vs Sikkim vs Rajasthan', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Rajasthan', 'Sikkim', 'Rajasthan']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Rajasthan vs Sikkim vs Rajasthan', width=550, height=320)
     return chart


</code>
"
1850,spatial_aggregation,Plot the top 8 states by average PM2.5 in 2024 as a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2024]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nlargest(8, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 8 States by Average PM2.5 in 2024', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2024]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nlargest(8, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 8 States by Average PM2.5 in 2024', width=500, height=300)
     return chart


</code>
"
1851,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Nagaland, Chandigarh, and Karnataka in 2019 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Nagaland', 'Chandigarh', 'Karnataka']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Nagaland, Chandigarh, UP – 2019', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Nagaland', 'Chandigarh', 'Karnataka']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Nagaland, Chandigarh, UP – 2019', width=550, height=320)
     return chart


</code>
"
1852,spatial_aggregation,Show a bar chart of the top 13 cities by median PM2.5 in 2019.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2019]
    df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
    df = df.nlargest(13, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
        y=alt.Y('city:N', sort='-x', title='City'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
        tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 13 Cities by Median PM2.5 in 2019', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2019]
     df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
     df = df.nlargest(13, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
         y=alt.Y('city:N', sort='-x', title='City'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
         tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 13 Cities by Median PM2.5 in 2019', width=500, height=300)
     return chart


</code>
"
1853,temporal_aggregation,Plot the weekly average PM2.5 for Moradabad in 2020 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Moradabad') & (data['Timestamp'].dt.year == 2020)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Moradabad 2020', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Moradabad') & (data['Timestamp'].dt.year == 2020)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Moradabad 2020', width=600, height=300)
     return chart


</code>
"
1854,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Madhya Pradesh, Himachal Pradesh, and Punjab from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Madhya Pradesh', 'Himachal Pradesh', 'Punjab']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Madhya Pradesh vs Himachal Pradesh vs Punjab', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Madhya Pradesh', 'Himachal Pradesh', 'Punjab']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Madhya Pradesh vs Himachal Pradesh vs Punjab', width=550, height=320)
     return chart


</code>
"
1855,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Haryana, Jammu and Kashmir, and Jammu and Kashmir in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Haryana', 'Jammu and Kashmir', 'Jammu and Kashmir']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Haryana, Jammu and Kashmir, UP – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Haryana', 'Jammu and Kashmir', 'Jammu and Kashmir']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Haryana, Jammu and Kashmir, UP – 2022', width=550, height=320)
     return chart


</code>
"
1856,specific_pattern,Show a cumulative area chart of PM2.5 readings for Srinagar across 2022.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Srinagar') & (data['Timestamp'].dt.year == 2022)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Srinagar 2022', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Srinagar') & (data['Timestamp'].dt.year == 2022)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Srinagar 2022', width=600, height=300)
     return chart


</code>
"
1857,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for West Bengal stations in 2023, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'West Bengal') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – West Bengal Stations 2023', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'West Bengal') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – West Bengal Stations 2023', width=450, height=350)


</code>
"
1858,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Himachal Pradesh, Meghalaya, and Rajasthan from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Himachal Pradesh', 'Meghalaya', 'Rajasthan']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Himachal Pradesh vs Meghalaya vs Rajasthan', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Himachal Pradesh', 'Meghalaya', 'Rajasthan']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Himachal Pradesh vs Meghalaya vs Rajasthan', width=550, height=320)
     return chart


</code>
"
1859,spatial_aggregation,"Show the top 11 states by average PM10 in 2024 as a bar chart, sorted in descending order.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2024]
    df = df.groupby('state')['PM10'].mean().reset_index().dropna()
    df = df.nlargest(11, 'PM10')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Top 11 States by Average PM10 in 2024', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2024]
     df = df.groupby('state')['PM10'].mean().reset_index().dropna()
     df = df.nlargest(11, 'PM10')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Top 11 States by Average PM10 in 2024', width=500, height=300)
     return chart


</code>
"
1860,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Mandideep, Ankleshwar, and Hanumangarh in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Mandideep', 'Ankleshwar', 'Hanumangarh']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Mandideep vs Ankleshwar vs Hanumangarh – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Mandideep', 'Ankleshwar', 'Hanumangarh']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Mandideep vs Ankleshwar vs Hanumangarh – 2024', width=550, height=320)
     return chart


</code>
"
1861,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Tamil Nadu, Meghalaya, and Bihar across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Tamil Nadu', 'Meghalaya', 'Bihar']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Tamil Nadu', 'Meghalaya', 'Bihar']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
1862,specific_pattern,Plot the rolling 30-day average PM2.5 for Tripura in 2019 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Tripura') & (data['Timestamp'].dt.year == 2019)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Tripura 2019', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Tripura') & (data['Timestamp'].dt.year == 2019)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Tripura 2019', width=600, height=300)


</code>
"
1863,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Andhra Pradesh, Uttarakhand, and Haryana in 2018 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Andhra Pradesh', 'Uttarakhand', 'Haryana']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2018)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Andhra Pradesh, Uttarakhand, UP – 2018', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Andhra Pradesh', 'Uttarakhand', 'Haryana']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2018)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Andhra Pradesh, Uttarakhand, UP – 2018', width=550, height=320)
     return chart


</code>
"
1864,specific_pattern,Plot the rolling 30-day average PM2.5 for Assam in 2019 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Assam') & (data['Timestamp'].dt.year == 2019)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Assam 2019', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Assam') & (data['Timestamp'].dt.year == 2019)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Assam 2019', width=600, height=300)


</code>
"
1865,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Himachal Pradesh, Meghalaya, and Jammu and Kashmir in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Himachal Pradesh', 'Meghalaya', 'Jammu and Kashmir']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Himachal Pradesh, Meghalaya, UP – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Himachal Pradesh', 'Meghalaya', 'Jammu and Kashmir']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Himachal Pradesh, Meghalaya, UP – 2024', width=550, height=320)
     return chart


</code>
"
1866,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Kollam, Chandrapur, and Akola in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Kollam', 'Chandrapur', 'Akola']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Kollam vs Chandrapur vs Akola – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Kollam', 'Chandrapur', 'Akola']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Kollam vs Chandrapur vs Akola – 2024', width=550, height=320)
     return chart


</code>
"
1867,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Sagar, Rajsamand, and Durgapur in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Sagar', 'Rajsamand', 'Durgapur']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Sagar vs Rajsamand vs Durgapur – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Sagar', 'Rajsamand', 'Durgapur']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Sagar vs Rajsamand vs Durgapur – 2024', width=550, height=320)
     return chart


</code>
"
1868,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Siwan, Jodhpur, and Karauli in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Siwan', 'Jodhpur', 'Karauli']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Siwan vs Jodhpur vs Karauli – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Siwan', 'Jodhpur', 'Karauli']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Siwan vs Jodhpur vs Karauli – 2024', width=550, height=320)
     return chart


</code>
"
1869,temporal_aggregation,Show the monthly average PM10 trend for Dindigul from 2017 to 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Dindigul'].copy()
    df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Dindigul (2017–2022)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Dindigul'].copy()
     df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Dindigul (2017–2022)', width=600, height=300)
     return chart


</code>
"
1870,spatial_aggregation,Visualize the bottom 8 states with the lowest average PM2.5 in 2022 using a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2022]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nsmallest(8, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Bottom 8 States by Average PM2.5 in 2022', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2022]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nsmallest(8, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Bottom 8 States by Average PM2.5 in 2022', width=500, height=300)
     return chart


</code>
"
1871,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Ujjain, Milupara, and Amaravati in 2019 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Ujjain', 'Milupara', 'Amaravati']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Ujjain vs Milupara vs Amaravati – 2019', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Ujjain', 'Milupara', 'Amaravati']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Ujjain vs Milupara vs Amaravati – 2019', width=550, height=320)
     return chart


</code>
"
1872,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Odisha stations in 2017, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Odisha') & (data['Timestamp'].dt.year == 2017)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Odisha Stations 2017', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Odisha') & (data['Timestamp'].dt.year == 2017)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Odisha Stations 2017', width=450, height=350)


</code>
"
1873,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Chandigarh, Jammu and Kashmir, and Andhra Pradesh across 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Chandigarh', 'Jammu and Kashmir', 'Andhra Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Chandigarh', 'Jammu and Kashmir', 'Andhra Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
     return chart


</code>
"
1874,temporal_aggregation,Plot the weekly average PM2.5 for Kota in 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Kota') & (data['Timestamp'].dt.year == 2024)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Kota 2024', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Kota') & (data['Timestamp'].dt.year == 2024)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Kota 2024', width=600, height=300)
     return chart


</code>
"
1875,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Mizoram, Odisha, and Manipur from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Mizoram', 'Odisha', 'Manipur']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Mizoram vs Odisha vs Manipur', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Mizoram', 'Odisha', 'Manipur']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Mizoram vs Odisha vs Manipur', width=550, height=320)
     return chart


</code>
"
1876,temporal_aggregation,Show the monthly average PM2.5 for Khurja in 2024 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Khurja') & (data['Timestamp'].dt.year == 2024)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Khurja 2024', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Khurja') & (data['Timestamp'].dt.year == 2024)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Khurja 2024', width=450, height=280)


</code>
"
1877,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Tamil Nadu, Gujarat, and Sikkim in 2019 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Tamil Nadu', 'Gujarat', 'Sikkim']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Tamil Nadu, Gujarat, UP – 2019', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Tamil Nadu', 'Gujarat', 'Sikkim']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Tamil Nadu, Gujarat, UP – 2019', width=550, height=320)
     return chart


</code>
"
1878,spatio_temporal_aggregation,"Visualize the monthly average PM10 for West Bengal, Rajasthan, and Uttar Pradesh in 2021 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['West Bengal', 'Rajasthan', 'Uttar Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: West Bengal, Rajasthan, UP – 2021', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['West Bengal', 'Rajasthan', 'Uttar Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: West Bengal, Rajasthan, UP – 2021', width=550, height=320)
     return chart


</code>
"
1879,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Assam stations in 2019, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Assam') & (data['Timestamp'].dt.year == 2019)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Assam Stations 2019', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Assam') & (data['Timestamp'].dt.year == 2019)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Assam Stations 2019', width=450, height=350)


</code>
"
1880,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Madhya Pradesh, Kerala, and Tamil Nadu from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Madhya Pradesh', 'Kerala', 'Tamil Nadu']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Madhya Pradesh vs Kerala vs Tamil Nadu', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Madhya Pradesh', 'Kerala', 'Tamil Nadu']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Madhya Pradesh vs Kerala vs Tamil Nadu', width=550, height=320)
     return chart


</code>
"
1881,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Mizoram, Chandigarh, and Telangana from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Mizoram', 'Chandigarh', 'Telangana']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Mizoram vs Chandigarh vs Telangana', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Mizoram', 'Chandigarh', 'Telangana']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Mizoram vs Chandigarh vs Telangana', width=550, height=320)
     return chart


</code>
"
1882,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Manipur stations in 2020, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Manipur') & (data['Timestamp'].dt.year == 2020)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Manipur Stations 2020', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Manipur') & (data['Timestamp'].dt.year == 2020)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Manipur Stations 2020', width=450, height=350)


</code>
"
1883,temporal_aggregation,Show the monthly average PM2.5 for Rishikesh in 2018 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Rishikesh') & (data['Timestamp'].dt.year == 2018)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Rishikesh 2018', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Rishikesh') & (data['Timestamp'].dt.year == 2018)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Rishikesh 2018', width=450, height=280)


</code>
"
1884,temporal_aggregation,Show the monthly average PM10 trend for Pune from 2017 to 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Pune'].copy()
    df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Pune (2017–2022)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Pune'].copy()
     df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Pune (2017–2022)', width=600, height=300)
     return chart


</code>
"
1885,spatial_aggregation,"Show the top 7 states by average PM10 in 2023 as a bar chart, sorted in descending order.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2023]
    df = df.groupby('state')['PM10'].mean().reset_index().dropna()
    df = df.nlargest(7, 'PM10')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Top 7 States by Average PM10 in 2023', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2023]
     df = df.groupby('state')['PM10'].mean().reset_index().dropna()
     df = df.nlargest(7, 'PM10')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Top 7 States by Average PM10 in 2023', width=500, height=300)
     return chart


</code>
"
1886,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Chandigarh, Punjab, and Karnataka across 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Chandigarh', 'Punjab', 'Karnataka']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Chandigarh', 'Punjab', 'Karnataka']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
     return chart


</code>
"
1887,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Sikkim, Punjab, and Punjab across 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Sikkim', 'Punjab', 'Punjab']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Sikkim', 'Punjab', 'Punjab']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
     return chart


</code>
"
1888,temporal_aggregation,Show the monthly average PM10 trend for Talcher from 2017 to 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Talcher'].copy()
    df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Talcher (2017–2022)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Talcher'].copy()
     df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Talcher (2017–2022)', width=600, height=300)
     return chart


</code>
"
1889,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Jammu and Kashmir, Sikkim, and Karnataka across 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Jammu and Kashmir', 'Sikkim', 'Karnataka']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Jammu and Kashmir', 'Sikkim', 'Karnataka']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
     return chart


</code>
"
1890,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Mizoram, Nagaland, and Uttar Pradesh across 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Mizoram', 'Nagaland', 'Uttar Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Mizoram', 'Nagaland', 'Uttar Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
     return chart


</code>
"
1891,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Karnal, Hyderabad, and Boisar in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Karnal', 'Hyderabad', 'Boisar']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Karnal vs Hyderabad vs Boisar – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Karnal', 'Hyderabad', 'Boisar']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Karnal vs Hyderabad vs Boisar – 2022', width=550, height=320)
     return chart


</code>
"
1892,spatial_aggregation,Visualize the bottom 13 states with the lowest average PM2.5 in 2022 using a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2022]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nsmallest(13, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Bottom 13 States by Average PM2.5 in 2022', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2022]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nsmallest(13, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Bottom 13 States by Average PM2.5 in 2022', width=500, height=300)
     return chart


</code>
"
1893,temporal_aggregation,Show the monthly average PM2.5 for Sirohi in 2017 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Sirohi') & (data['Timestamp'].dt.year == 2017)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Sirohi 2017', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Sirohi') & (data['Timestamp'].dt.year == 2017)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Sirohi 2017', width=450, height=280)


</code>
"
1894,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Haryana, Tripura, and Assam across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Haryana', 'Tripura', 'Assam']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Haryana', 'Tripura', 'Assam']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
1895,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Rajasthan, Jammu and Kashmir, and Assam from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Rajasthan', 'Jammu and Kashmir', 'Assam']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Rajasthan vs Jammu and Kashmir vs Assam', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Rajasthan', 'Jammu and Kashmir', 'Assam']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Rajasthan vs Jammu and Kashmir vs Assam', width=550, height=320)
     return chart


</code>
"
1896,temporal_aggregation,Show the monthly average PM2.5 for Virudhunagar in 2024 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Virudhunagar') & (data['Timestamp'].dt.year == 2024)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Virudhunagar 2024', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Virudhunagar') & (data['Timestamp'].dt.year == 2024)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Virudhunagar 2024', width=450, height=280)


</code>
"
1897,spatial_aggregation,Visualize the bottom 9 states with the lowest average PM2.5 in 2020 using a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2020]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nsmallest(9, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Bottom 9 States by Average PM2.5 in 2020', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2020]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nsmallest(9, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Bottom 9 States by Average PM2.5 in 2020', width=500, height=300)
     return chart


</code>
"
1898,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Maharashtra, Jharkhand, and Manipur in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Maharashtra', 'Jharkhand', 'Manipur']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Maharashtra, Jharkhand, UP – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Maharashtra', 'Jharkhand', 'Manipur']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Maharashtra, Jharkhand, UP – 2023', width=550, height=320)
     return chart


</code>
"
1899,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Bihar, Kerala, and Chhattisgarh in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Bihar', 'Kerala', 'Chhattisgarh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Bihar, Kerala, UP – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Bihar', 'Kerala', 'Chhattisgarh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Bihar, Kerala, UP – 2023', width=550, height=320)
     return chart


</code>
"
1900,specific_pattern,Plot the rolling 30-day average PM2.5 for Andhra Pradesh in 2018 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Andhra Pradesh') & (data['Timestamp'].dt.year == 2018)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Andhra Pradesh 2018', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Andhra Pradesh') & (data['Timestamp'].dt.year == 2018)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Andhra Pradesh 2018', width=600, height=300)


</code>
"
1901,specific_pattern,Plot the rolling 30-day average PM2.5 for Kerala in 2019 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Kerala') & (data['Timestamp'].dt.year == 2019)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Kerala 2019', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Kerala') & (data['Timestamp'].dt.year == 2019)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Kerala 2019', width=600, height=300)


</code>
"
1902,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Jammu and Kashmir, Telangana, and Odisha from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Jammu and Kashmir', 'Telangana', 'Odisha']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Jammu and Kashmir vs Telangana vs Odisha', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Jammu and Kashmir', 'Telangana', 'Odisha']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Jammu and Kashmir vs Telangana vs Odisha', width=550, height=320)
     return chart


</code>
"
1903,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Mizoram stations in 2018, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Mizoram') & (data['Timestamp'].dt.year == 2018)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Mizoram Stations 2018', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Mizoram') & (data['Timestamp'].dt.year == 2018)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Mizoram Stations 2018', width=450, height=350)


</code>
"
1904,temporal_aggregation,Plot the weekly average PM2.5 for Udaipur in 2019 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Udaipur') & (data['Timestamp'].dt.year == 2019)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Udaipur 2019', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Udaipur') & (data['Timestamp'].dt.year == 2019)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Udaipur 2019', width=600, height=300)
     return chart


</code>
"
1905,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Himachal Pradesh, Chhattisgarh, and Tamil Nadu from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Himachal Pradesh', 'Chhattisgarh', 'Tamil Nadu']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Himachal Pradesh vs Chhattisgarh vs Tamil Nadu', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Himachal Pradesh', 'Chhattisgarh', 'Tamil Nadu']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Himachal Pradesh vs Chhattisgarh vs Tamil Nadu', width=550, height=320)
     return chart


</code>
"
1906,spatial_aggregation,"Show the top 15 states by average PM10 in 2018 as a bar chart, sorted in descending order.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2018]
    df = df.groupby('state')['PM10'].mean().reset_index().dropna()
    df = df.nlargest(15, 'PM10')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Top 15 States by Average PM10 in 2018', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2018]
     df = df.groupby('state')['PM10'].mean().reset_index().dropna()
     df = df.nlargest(15, 'PM10')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Top 15 States by Average PM10 in 2018', width=500, height=300)
     return chart


</code>
"
1907,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Gujarat, Kerala, and West Bengal across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Gujarat', 'Kerala', 'West Bengal']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Gujarat', 'Kerala', 'West Bengal']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
1908,specific_pattern,Show a cumulative area chart of PM2.5 readings for Surat across 2022.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Surat') & (data['Timestamp'].dt.year == 2022)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Surat 2022', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Surat') & (data['Timestamp'].dt.year == 2022)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Surat 2022', width=600, height=300)
     return chart


</code>
"
1909,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Maharashtra, Andhra Pradesh, and Rajasthan in 2021 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Maharashtra', 'Andhra Pradesh', 'Rajasthan']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Maharashtra, Andhra Pradesh, UP – 2021', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Maharashtra', 'Andhra Pradesh', 'Rajasthan']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Maharashtra, Andhra Pradesh, UP – 2021', width=550, height=320)
     return chart


</code>
"
1910,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Madhya Pradesh, Maharashtra, and Jharkhand from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Madhya Pradesh', 'Maharashtra', 'Jharkhand']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Madhya Pradesh vs Maharashtra vs Jharkhand', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Madhya Pradesh', 'Maharashtra', 'Jharkhand']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Madhya Pradesh vs Maharashtra vs Jharkhand', width=550, height=320)
     return chart


</code>
"
1911,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Karnataka, Chandigarh, and Nagaland in 2017 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Karnataka', 'Chandigarh', 'Nagaland']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2017)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Karnataka, Chandigarh, UP – 2017', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Karnataka', 'Chandigarh', 'Nagaland']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2017)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Karnataka, Chandigarh, UP – 2017', width=550, height=320)
     return chart


</code>
"
1912,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Arunachal Pradesh, Maharashtra, and Sikkim from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Arunachal Pradesh', 'Maharashtra', 'Sikkim']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Arunachal Pradesh vs Maharashtra vs Sikkim', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Arunachal Pradesh', 'Maharashtra', 'Sikkim']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Arunachal Pradesh vs Maharashtra vs Sikkim', width=550, height=320)
     return chart


</code>
"
1913,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Damoh, Malegaon, and Korba in 2020 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Damoh', 'Malegaon', 'Korba']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2020)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Damoh vs Malegaon vs Korba – 2020', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Damoh', 'Malegaon', 'Korba']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2020)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Damoh vs Malegaon vs Korba – 2020', width=550, height=320)
     return chart


</code>
"
1914,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Punjab, Madhya Pradesh, and Maharashtra in 2019 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Punjab', 'Madhya Pradesh', 'Maharashtra']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Punjab, Madhya Pradesh, UP – 2019', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Punjab', 'Madhya Pradesh', 'Maharashtra']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Punjab, Madhya Pradesh, UP – 2019', width=550, height=320)
     return chart


</code>
"
1915,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Chandigarh, Sikkim, and Chandigarh across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Chandigarh', 'Sikkim', 'Chandigarh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Chandigarh', 'Sikkim', 'Chandigarh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
1916,temporal_aggregation,Show the monthly average PM2.5 for Buxar in 2020 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Buxar') & (data['Timestamp'].dt.year == 2020)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Buxar 2020', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Buxar') & (data['Timestamp'].dt.year == 2020)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Buxar 2020', width=450, height=280)


</code>
"
1917,temporal_aggregation,Show the monthly average PM10 trend for Palkalaiperur from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Palkalaiperur'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Palkalaiperur (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Palkalaiperur'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Palkalaiperur (2019–2024)', width=600, height=300)
     return chart


</code>
"
1918,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Rajasthan, Uttar Pradesh, and Uttarakhand across 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Rajasthan', 'Uttar Pradesh', 'Uttarakhand']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Rajasthan', 'Uttar Pradesh', 'Uttarakhand']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
     return chart


</code>
"
1919,specific_pattern,Plot the rolling 30-day average PM2.5 for Manipur in 2019 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Manipur') & (data['Timestamp'].dt.year == 2019)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Manipur 2019', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Manipur') & (data['Timestamp'].dt.year == 2019)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Manipur 2019', width=600, height=300)


</code>
"
1920,temporal_aggregation,Plot the weekly average PM2.5 for Nagapattinam in 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Nagapattinam') & (data['Timestamp'].dt.year == 2024)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Nagapattinam 2024', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Nagapattinam') & (data['Timestamp'].dt.year == 2024)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Nagapattinam 2024', width=600, height=300)
     return chart


</code>
"
1921,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Madurai, Gwalior, and Sagar in 2020 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Madurai', 'Gwalior', 'Sagar']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2020)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Madurai vs Gwalior vs Sagar – 2020', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Madurai', 'Gwalior', 'Sagar']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2020)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Madurai vs Gwalior vs Sagar – 2020', width=550, height=320)
     return chart


</code>
"
1922,temporal_aggregation,Show the monthly average PM2.5 for Bhubaneswar in 2023 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Bhubaneswar') & (data['Timestamp'].dt.year == 2023)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Bhubaneswar 2023', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Bhubaneswar') & (data['Timestamp'].dt.year == 2023)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Bhubaneswar 2023', width=450, height=280)


</code>
"
1923,temporal_aggregation,Show the monthly average PM2.5 for Kashipur in 2019 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Kashipur') & (data['Timestamp'].dt.year == 2019)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Kashipur 2019', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Kashipur') & (data['Timestamp'].dt.year == 2019)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Kashipur 2019', width=450, height=280)


</code>
"
1924,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Jharkhand, Sikkim, and Nagaland across 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Jharkhand', 'Sikkim', 'Nagaland']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Jharkhand', 'Sikkim', 'Nagaland']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
     return chart


</code>
"
1925,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Tiruchirappalli, Charkhi Dadri, and Bilaspur in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Tiruchirappalli', 'Charkhi Dadri', 'Bilaspur']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Tiruchirappalli vs Charkhi Dadri vs Bilaspur – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Tiruchirappalli', 'Charkhi Dadri', 'Bilaspur']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Tiruchirappalli vs Charkhi Dadri vs Bilaspur – 2022', width=550, height=320)
     return chart


</code>
"
1926,temporal_aggregation,Show a monthly bar chart of the number of days Tamil Nadu exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2017.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Tamil Nadu') & (data['Timestamp'].dt.year == 2017)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Tamil Nadu Exceeded WHO PM2.5 Guideline per Month – 2017', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Tamil Nadu') & (data['Timestamp'].dt.year == 2017)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Tamil Nadu Exceeded WHO PM2.5 Guideline per Month – 2017', width=500, height=300)
     return chart


</code>
"
1927,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Delhi, Uttarakhand, and Assam from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Delhi', 'Uttarakhand', 'Assam']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Delhi vs Uttarakhand vs Assam', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Delhi', 'Uttarakhand', 'Assam']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Delhi vs Uttarakhand vs Assam', width=550, height=320)
     return chart


</code>
"
1928,temporal_aggregation,Show a monthly bar chart of the number of days Tripura exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2021.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Tripura') & (data['Timestamp'].dt.year == 2021)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Tripura Exceeded WHO PM2.5 Guideline per Month – 2021', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Tripura') & (data['Timestamp'].dt.year == 2021)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Tripura Exceeded WHO PM2.5 Guideline per Month – 2021', width=500, height=300)
     return chart


</code>
"
1929,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Tamil Nadu, Tamil Nadu, and Punjab across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Tamil Nadu', 'Tamil Nadu', 'Punjab']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Tamil Nadu', 'Tamil Nadu', 'Punjab']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
1930,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Raichur, Virudhunagar, and Chamarajanagar in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Raichur', 'Virudhunagar', 'Chamarajanagar']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Raichur vs Virudhunagar vs Chamarajanagar – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Raichur', 'Virudhunagar', 'Chamarajanagar']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Raichur vs Virudhunagar vs Chamarajanagar – 2023', width=550, height=320)
     return chart


</code>
"
1931,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Nagaland, Uttar Pradesh, and Meghalaya across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Nagaland', 'Uttar Pradesh', 'Meghalaya']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Nagaland', 'Uttar Pradesh', 'Meghalaya']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
1932,temporal_aggregation,Show the monthly average PM2.5 for Nagaur in 2020 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Nagaur') & (data['Timestamp'].dt.year == 2020)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Nagaur 2020', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Nagaur') & (data['Timestamp'].dt.year == 2020)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Nagaur 2020', width=450, height=280)


</code>
"
1933,spatial_aggregation,Visualize the bottom 9 states with the lowest average PM2.5 in 2023 using a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2023]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nsmallest(9, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Bottom 9 States by Average PM2.5 in 2023', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2023]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nsmallest(9, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Bottom 9 States by Average PM2.5 in 2023', width=500, height=300)
     return chart


</code>
"
1934,spatial_aggregation,"Show the top 12 states by average PM10 in 2024 as a bar chart, sorted in descending order.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2024]
    df = df.groupby('state')['PM10'].mean().reset_index().dropna()
    df = df.nlargest(12, 'PM10')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Top 12 States by Average PM10 in 2024', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2024]
     df = df.groupby('state')['PM10'].mean().reset_index().dropna()
     df = df.nlargest(12, 'PM10')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Top 12 States by Average PM10 in 2024', width=500, height=300)
     return chart


</code>
"
1935,temporal_aggregation,Show the monthly average PM2.5 for Suakati in 2023 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Suakati') & (data['Timestamp'].dt.year == 2023)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Suakati 2023', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Suakati') & (data['Timestamp'].dt.year == 2023)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Suakati 2023', width=450, height=280)


</code>
"
1936,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Odisha stations in 2018, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Odisha') & (data['Timestamp'].dt.year == 2018)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Odisha Stations 2018', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Odisha') & (data['Timestamp'].dt.year == 2018)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Odisha Stations 2018', width=450, height=350)


</code>
"
1937,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Puducherry, Telangana, and Odisha in 2019 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Puducherry', 'Telangana', 'Odisha']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Puducherry, Telangana, UP – 2019', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Puducherry', 'Telangana', 'Odisha']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Puducherry, Telangana, UP – 2019', width=550, height=320)
     return chart


</code>
"
1938,temporal_aggregation,Show the monthly average PM2.5 for Chhal in 2023 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Chhal') & (data['Timestamp'].dt.year == 2023)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Chhal 2023', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Chhal') & (data['Timestamp'].dt.year == 2023)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Chhal 2023', width=450, height=280)


</code>
"
1939,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Telangana stations in 2018, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Telangana') & (data['Timestamp'].dt.year == 2018)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Telangana Stations 2018', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Telangana') & (data['Timestamp'].dt.year == 2018)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Telangana Stations 2018', width=450, height=350)


</code>
"
1940,specific_pattern,Show a cumulative area chart of PM2.5 readings for Kaithal across 2021.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Kaithal') & (data['Timestamp'].dt.year == 2021)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Kaithal 2021', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Kaithal') & (data['Timestamp'].dt.year == 2021)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Kaithal 2021', width=600, height=300)
     return chart


</code>
"
1941,specific_pattern,Show a cumulative area chart of PM2.5 readings for Mangalore across 2021.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Mangalore') & (data['Timestamp'].dt.year == 2021)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Mangalore 2021', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Mangalore') & (data['Timestamp'].dt.year == 2021)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Mangalore 2021', width=600, height=300)
     return chart


</code>
"
1942,temporal_aggregation,Plot the weekly average PM2.5 for Hapur in 2019 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Hapur') & (data['Timestamp'].dt.year == 2019)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Hapur 2019', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Hapur') & (data['Timestamp'].dt.year == 2019)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Hapur 2019', width=600, height=300)
     return chart


</code>
"
1943,spatial_aggregation,Plot the top 11 states by average PM2.5 in 2018 as a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2018]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nlargest(11, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 11 States by Average PM2.5 in 2018', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2018]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nlargest(11, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 11 States by Average PM2.5 in 2018', width=500, height=300)
     return chart


</code>
"
1944,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Silchar, Malegaon, and Nagaur in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Silchar', 'Malegaon', 'Nagaur']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Silchar vs Malegaon vs Nagaur – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Silchar', 'Malegaon', 'Nagaur']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Silchar vs Malegaon vs Nagaur – 2023', width=550, height=320)
     return chart


</code>
"
1945,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for West Bengal, Karnataka, and Mizoram across 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['West Bengal', 'Karnataka', 'Mizoram']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['West Bengal', 'Karnataka', 'Mizoram']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
     return chart


</code>
"
1946,temporal_aggregation,Show a monthly bar chart of the number of days Chhattisgarh exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2022.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Chhattisgarh') & (data['Timestamp'].dt.year == 2022)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Chhattisgarh Exceeded WHO PM2.5 Guideline per Month – 2022', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Chhattisgarh') & (data['Timestamp'].dt.year == 2022)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Chhattisgarh Exceeded WHO PM2.5 Guideline per Month – 2022', width=500, height=300)
     return chart


</code>
"
1947,temporal_aggregation,Show the monthly average PM2.5 for Kolar in 2018 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Kolar') & (data['Timestamp'].dt.year == 2018)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Kolar 2018', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Kolar') & (data['Timestamp'].dt.year == 2018)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Kolar 2018', width=450, height=280)


</code>
"
1948,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Kerala, Delhi, and Assam in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Kerala', 'Delhi', 'Assam']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Kerala, Delhi, UP – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Kerala', 'Delhi', 'Assam']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Kerala, Delhi, UP – 2024', width=550, height=320)
     return chart


</code>
"
1949,spatio_temporal_aggregation,Show a heatmap of average PM2.5 for the top 12 most polluted states by month for 2017.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    top8 = data.groupby('state')['PM2.5'].mean().nlargest(12).index.tolist()
    df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2017)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_rect().encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('state:N', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
        tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Heatmap – Top 12 Polluted States by Month (2017)', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     top8 = data.groupby('state')['PM2.5'].mean().nlargest(12).index.tolist()
     df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2017)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_rect().encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('state:N', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
         tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Heatmap – Top 12 Polluted States by Month (2017)', width=500, height=300)
     return chart


</code>
"
1950,temporal_aggregation,Plot the weekly average PM2.5 for Nagpur in 2020 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Nagpur') & (data['Timestamp'].dt.year == 2020)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Nagpur 2020', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Nagpur') & (data['Timestamp'].dt.year == 2020)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Nagpur 2020', width=600, height=300)
     return chart


</code>
"
1951,temporal_aggregation,Show the monthly average PM10 trend for Chhapra from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Chhapra'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Chhapra (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Chhapra'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Chhapra (2019–2024)', width=600, height=300)
     return chart


</code>
"
1952,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Meghalaya, Jharkhand, and Assam across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Meghalaya', 'Jharkhand', 'Assam']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Meghalaya', 'Jharkhand', 'Assam']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
1953,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Uttar Pradesh, Chhattisgarh, and Nagaland from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Uttar Pradesh', 'Chhattisgarh', 'Nagaland']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Uttar Pradesh vs Chhattisgarh vs Nagaland', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Uttar Pradesh', 'Chhattisgarh', 'Nagaland']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Uttar Pradesh vs Chhattisgarh vs Nagaland', width=550, height=320)
     return chart


</code>
"
1954,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Jammu and Kashmir, Jharkhand, and Manipur in 2019 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Jammu and Kashmir', 'Jharkhand', 'Manipur']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Jammu and Kashmir, Jharkhand, UP – 2019', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Jammu and Kashmir', 'Jharkhand', 'Manipur']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Jammu and Kashmir, Jharkhand, UP – 2019', width=550, height=320)
     return chart


</code>
"
1955,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Chandigarh, Delhi, and Delhi from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Chandigarh', 'Delhi', 'Delhi']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Chandigarh vs Delhi vs Delhi', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Chandigarh', 'Delhi', 'Delhi']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Chandigarh vs Delhi vs Delhi', width=550, height=320)
     return chart


</code>
"
1956,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Jammu and Kashmir, Odisha, and Karnataka across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Jammu and Kashmir', 'Odisha', 'Karnataka']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Jammu and Kashmir', 'Odisha', 'Karnataka']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
1957,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Himachal Pradesh, Puducherry, and Karnataka across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Himachal Pradesh', 'Puducherry', 'Karnataka']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Himachal Pradesh', 'Puducherry', 'Karnataka']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
1958,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Pali, Dewas, and Durgapur in 2020 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Pali', 'Dewas', 'Durgapur']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2020)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Pali vs Dewas vs Durgapur – 2020', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Pali', 'Dewas', 'Durgapur']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2020)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Pali vs Dewas vs Durgapur – 2020', width=550, height=320)
     return chart


</code>
"
1959,spatial_aggregation,Plot the top 10 states by average PM2.5 in 2018 as a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2018]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nlargest(10, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 10 States by Average PM2.5 in 2018', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2018]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nlargest(10, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 10 States by Average PM2.5 in 2018', width=500, height=300)
     return chart


</code>
"
1960,temporal_aggregation,Show the monthly average PM10 trend for Rishikesh from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Rishikesh'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Rishikesh (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Rishikesh'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Rishikesh (2019–2024)', width=600, height=300)
     return chart


</code>
"
1961,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Himachal Pradesh, Haryana, and Kerala across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Himachal Pradesh', 'Haryana', 'Kerala']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Himachal Pradesh', 'Haryana', 'Kerala']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
1962,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Tripura, Assam, and Puducherry across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Tripura', 'Assam', 'Puducherry']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Tripura', 'Assam', 'Puducherry']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
1963,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Puducherry, Sikkim, and Himachal Pradesh across 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Puducherry', 'Sikkim', 'Himachal Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Puducherry', 'Sikkim', 'Himachal Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
     return chart


</code>
"
1964,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Madhya Pradesh stations in 2017, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Madhya Pradesh') & (data['Timestamp'].dt.year == 2017)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Madhya Pradesh Stations 2017', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Madhya Pradesh') & (data['Timestamp'].dt.year == 2017)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Madhya Pradesh Stations 2017', width=450, height=350)


</code>
"
1965,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Jharkhand, Punjab, and Madhya Pradesh across 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Jharkhand', 'Punjab', 'Madhya Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Jharkhand', 'Punjab', 'Madhya Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
     return chart


</code>
"
1966,spatial_aggregation,Show a bar chart of the top 10 cities by median PM2.5 in 2019.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2019]
    df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
    df = df.nlargest(10, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
        y=alt.Y('city:N', sort='-x', title='City'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
        tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 10 Cities by Median PM2.5 in 2019', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2019]
     df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
     df = df.nlargest(10, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
         y=alt.Y('city:N', sort='-x', title='City'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
         tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 10 Cities by Median PM2.5 in 2019', width=500, height=300)
     return chart


</code>
"
1967,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Tamil Nadu, Mizoram, and Delhi across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Tamil Nadu', 'Mizoram', 'Delhi']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Tamil Nadu', 'Mizoram', 'Delhi']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
1968,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Jalandhar, Ujjain, and Thanjavur in 2019 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Jalandhar', 'Ujjain', 'Thanjavur']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Jalandhar vs Ujjain vs Thanjavur – 2019', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Jalandhar', 'Ujjain', 'Thanjavur']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Jalandhar vs Ujjain vs Thanjavur – 2019', width=550, height=320)
     return chart


</code>
"
1969,specific_pattern,Show a cumulative area chart of PM2.5 readings for Balasore across 2024.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Balasore') & (data['Timestamp'].dt.year == 2024)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Balasore 2024', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Balasore') & (data['Timestamp'].dt.year == 2024)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Balasore 2024', width=600, height=300)
     return chart


</code>
"
1970,temporal_aggregation,Plot the weekly average PM2.5 for Raipur in 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Raipur') & (data['Timestamp'].dt.year == 2024)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Raipur 2024', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Raipur') & (data['Timestamp'].dt.year == 2024)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Raipur 2024', width=600, height=300)
     return chart


</code>
"
1971,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Andhra Pradesh, Jammu and Kashmir, and Punjab from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Andhra Pradesh', 'Jammu and Kashmir', 'Punjab']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Andhra Pradesh vs Jammu and Kashmir vs Punjab', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Andhra Pradesh', 'Jammu and Kashmir', 'Punjab']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Andhra Pradesh vs Jammu and Kashmir vs Punjab', width=550, height=320)
     return chart


</code>
"
1972,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Assam, Himachal Pradesh, and Nagaland from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Assam', 'Himachal Pradesh', 'Nagaland']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Assam vs Himachal Pradesh vs Nagaland', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Assam', 'Himachal Pradesh', 'Nagaland']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Assam vs Himachal Pradesh vs Nagaland', width=550, height=320)
     return chart


</code>
"
1973,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Nagaland stations in 2023, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Nagaland') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Nagaland Stations 2023', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Nagaland') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Nagaland Stations 2023', width=450, height=350)


</code>
"
1974,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Andhra Pradesh stations in 2024, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Andhra Pradesh') & (data['Timestamp'].dt.year == 2024)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Andhra Pradesh Stations 2024', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Andhra Pradesh') & (data['Timestamp'].dt.year == 2024)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Andhra Pradesh Stations 2024', width=450, height=350)


</code>
"
1975,temporal_aggregation,Show the monthly average PM10 trend for Chandrapur from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Chandrapur'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Chandrapur (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Chandrapur'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Chandrapur (2019–2024)', width=600, height=300)
     return chart


</code>
"
1976,temporal_aggregation,Plot the weekly average PM2.5 for Bulandshahr in 2018 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Bulandshahr') & (data['Timestamp'].dt.year == 2018)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Bulandshahr 2018', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Bulandshahr') & (data['Timestamp'].dt.year == 2018)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Bulandshahr 2018', width=600, height=300)
     return chart


</code>
"
1977,temporal_aggregation,Show a monthly bar chart of the number of days Sikkim exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2024.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Sikkim') & (data['Timestamp'].dt.year == 2024)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Sikkim Exceeded WHO PM2.5 Guideline per Month – 2024', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Sikkim') & (data['Timestamp'].dt.year == 2024)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Sikkim Exceeded WHO PM2.5 Guideline per Month – 2024', width=500, height=300)
     return chart


</code>
"
1978,temporal_aggregation,Plot the weekly average PM2.5 for Karnal in 2020 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Karnal') & (data['Timestamp'].dt.year == 2020)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Karnal 2020', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Karnal') & (data['Timestamp'].dt.year == 2020)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Karnal 2020', width=600, height=300)
     return chart


</code>
"
1979,specific_pattern,Plot the rolling 30-day average PM2.5 for Sikkim in 2023 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Sikkim') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Sikkim 2023', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Sikkim') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Sikkim 2023', width=600, height=300)


</code>
"
1980,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Kerala stations in 2020, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Kerala') & (data['Timestamp'].dt.year == 2020)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Kerala Stations 2020', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Kerala') & (data['Timestamp'].dt.year == 2020)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Kerala Stations 2020', width=450, height=350)


</code>
"
1981,temporal_aggregation,Show the monthly average PM2.5 for Ankleshwar in 2023 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Ankleshwar') & (data['Timestamp'].dt.year == 2023)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Ankleshwar 2023', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Ankleshwar') & (data['Timestamp'].dt.year == 2023)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Ankleshwar 2023', width=450, height=280)


</code>
"
1982,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Odisha, Mizoram, and Maharashtra in 2018 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Odisha', 'Mizoram', 'Maharashtra']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2018)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Odisha, Mizoram, UP – 2018', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Odisha', 'Mizoram', 'Maharashtra']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2018)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Odisha, Mizoram, UP – 2018', width=550, height=320)
     return chart


</code>
"
1983,specific_pattern,Plot the rolling 30-day average PM2.5 for West Bengal in 2017 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'West Bengal') & (data['Timestamp'].dt.year == 2017)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – West Bengal 2017', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'West Bengal') & (data['Timestamp'].dt.year == 2017)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – West Bengal 2017', width=600, height=300)


</code>
"
1984,temporal_aggregation,Show the monthly average PM2.5 for Delhi in 2024 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Delhi') & (data['Timestamp'].dt.year == 2024)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Delhi 2024', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Delhi') & (data['Timestamp'].dt.year == 2024)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Delhi 2024', width=450, height=280)


</code>
"
1985,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Uttarakhand, Maharashtra, and Punjab in 2018 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Uttarakhand', 'Maharashtra', 'Punjab']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2018)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Uttarakhand, Maharashtra, UP – 2018', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Uttarakhand', 'Maharashtra', 'Punjab']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2018)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Uttarakhand, Maharashtra, UP – 2018', width=550, height=320)
     return chart


</code>
"
1986,temporal_aggregation,Show the monthly average PM2.5 for Chhal in 2019 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Chhal') & (data['Timestamp'].dt.year == 2019)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Chhal 2019', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Chhal') & (data['Timestamp'].dt.year == 2019)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Chhal 2019', width=450, height=280)


</code>
"
1987,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Madhya Pradesh, Nagaland, and Gujarat in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Madhya Pradesh', 'Nagaland', 'Gujarat']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Madhya Pradesh, Nagaland, UP – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Madhya Pradesh', 'Nagaland', 'Gujarat']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Madhya Pradesh, Nagaland, UP – 2023', width=550, height=320)
     return chart


</code>
"
1988,temporal_aggregation,Show a monthly bar chart of the number of days Madhya Pradesh exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2018.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Madhya Pradesh') & (data['Timestamp'].dt.year == 2018)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Madhya Pradesh Exceeded WHO PM2.5 Guideline per Month – 2018', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Madhya Pradesh') & (data['Timestamp'].dt.year == 2018)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Madhya Pradesh Exceeded WHO PM2.5 Guideline per Month – 2018', width=500, height=300)
     return chart


</code>
"
1989,specific_pattern,Plot the rolling 30-day average PM2.5 for Kerala in 2023 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Kerala') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Kerala 2023', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Kerala') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Kerala 2023', width=600, height=300)


</code>
"
1990,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Uttar Pradesh stations in 2019, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Uttar Pradesh') & (data['Timestamp'].dt.year == 2019)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Uttar Pradesh Stations 2019', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Uttar Pradesh') & (data['Timestamp'].dt.year == 2019)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Uttar Pradesh Stations 2019', width=450, height=350)


</code>
"
1991,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Muzaffarpur, Dharwad, and Bundi in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Muzaffarpur', 'Dharwad', 'Bundi']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Muzaffarpur vs Dharwad vs Bundi – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Muzaffarpur', 'Dharwad', 'Bundi']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Muzaffarpur vs Dharwad vs Bundi – 2024', width=550, height=320)
     return chart


</code>
"
1992,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Jammu and Kashmir stations in 2023, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Jammu and Kashmir') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Jammu and Kashmir Stations 2023', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Jammu and Kashmir') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Jammu and Kashmir Stations 2023', width=450, height=350)


</code>
"
1993,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Delhi, Maharashtra, and Haryana in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Delhi', 'Maharashtra', 'Haryana']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Delhi, Maharashtra, UP – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Delhi', 'Maharashtra', 'Haryana']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Delhi, Maharashtra, UP – 2024', width=550, height=320)
     return chart


</code>
"
1994,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Jammu and Kashmir, Haryana, and Himachal Pradesh in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Jammu and Kashmir', 'Haryana', 'Himachal Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Jammu and Kashmir, Haryana, UP – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Jammu and Kashmir', 'Haryana', 'Himachal Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Jammu and Kashmir, Haryana, UP – 2024', width=550, height=320)
     return chart


</code>
"
1995,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Uttar Pradesh, Mizoram, and Jharkhand from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Uttar Pradesh', 'Mizoram', 'Jharkhand']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Uttar Pradesh vs Mizoram vs Jharkhand', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Uttar Pradesh', 'Mizoram', 'Jharkhand']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Uttar Pradesh vs Mizoram vs Jharkhand', width=550, height=320)
     return chart


</code>
"
1996,specific_pattern,Plot the rolling 30-day average PM2.5 for Chandigarh in 2019 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Chandigarh') & (data['Timestamp'].dt.year == 2019)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Chandigarh 2019', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Chandigarh') & (data['Timestamp'].dt.year == 2019)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Chandigarh 2019', width=600, height=300)


</code>
"
1997,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Panchkula, Thiruvananthapuram, and Talcher in 2019 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Panchkula', 'Thiruvananthapuram', 'Talcher']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Panchkula vs Thiruvananthapuram vs Talcher – 2019', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Panchkula', 'Thiruvananthapuram', 'Talcher']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Panchkula vs Thiruvananthapuram vs Talcher – 2019', width=550, height=320)
     return chart


</code>
"
1998,temporal_aggregation,Show a monthly bar chart of the number of days Odisha exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2020.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Odisha') & (data['Timestamp'].dt.year == 2020)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Odisha Exceeded WHO PM2.5 Guideline per Month – 2020', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Odisha') & (data['Timestamp'].dt.year == 2020)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Odisha Exceeded WHO PM2.5 Guideline per Month – 2020', width=500, height=300)
     return chart


</code>
"
1999,temporal_aggregation,Show the monthly average PM2.5 for Sonipat in 2019 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Sonipat') & (data['Timestamp'].dt.year == 2019)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Sonipat 2019', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Sonipat') & (data['Timestamp'].dt.year == 2019)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Sonipat 2019', width=450, height=280)


</code>
"
2000,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Rajasthan, Jharkhand, and Delhi from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Rajasthan', 'Jharkhand', 'Delhi']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Rajasthan vs Jharkhand vs Delhi', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Rajasthan', 'Jharkhand', 'Delhi']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Rajasthan vs Jharkhand vs Delhi', width=550, height=320)
     return chart


</code>
"
2001,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Tamil Nadu, Haryana, and Rajasthan in 2018 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Tamil Nadu', 'Haryana', 'Rajasthan']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2018)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Tamil Nadu, Haryana, UP – 2018', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Tamil Nadu', 'Haryana', 'Rajasthan']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2018)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Tamil Nadu, Haryana, UP – 2018', width=550, height=320)
     return chart


</code>
"
2002,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Punjab, Chhattisgarh, and Madhya Pradesh from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Punjab', 'Chhattisgarh', 'Madhya Pradesh']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Punjab vs Chhattisgarh vs Madhya Pradesh', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Punjab', 'Chhattisgarh', 'Madhya Pradesh']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Punjab vs Chhattisgarh vs Madhya Pradesh', width=550, height=320)
     return chart


</code>
"
2003,temporal_aggregation,Plot the weekly average PM2.5 for Jalandhar in 2018 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Jalandhar') & (data['Timestamp'].dt.year == 2018)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Jalandhar 2018', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Jalandhar') & (data['Timestamp'].dt.year == 2018)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Jalandhar 2018', width=600, height=300)
     return chart


</code>
"
2004,temporal_aggregation,Show the monthly average PM2.5 for Byasanagar in 2020 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Byasanagar') & (data['Timestamp'].dt.year == 2020)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Byasanagar 2020', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Byasanagar') & (data['Timestamp'].dt.year == 2020)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Byasanagar 2020', width=450, height=280)


</code>
"
2005,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Rajasthan, Madhya Pradesh, and Uttar Pradesh across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Rajasthan', 'Madhya Pradesh', 'Uttar Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Rajasthan', 'Madhya Pradesh', 'Uttar Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
2006,temporal_aggregation,Show the monthly average PM2.5 for Rohtak in 2022 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Rohtak') & (data['Timestamp'].dt.year == 2022)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Rohtak 2022', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Rohtak') & (data['Timestamp'].dt.year == 2022)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Rohtak 2022', width=450, height=280)


</code>
"
2007,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Puducherry, Haryana, and Meghalaya in 2019 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Puducherry', 'Haryana', 'Meghalaya']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Puducherry, Haryana, UP – 2019', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Puducherry', 'Haryana', 'Meghalaya']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Puducherry, Haryana, UP – 2019', width=550, height=320)
     return chart


</code>
"
2008,specific_pattern,Plot the rolling 30-day average PM2.5 for Bihar in 2019 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Bihar') & (data['Timestamp'].dt.year == 2019)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Bihar 2019', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Bihar') & (data['Timestamp'].dt.year == 2019)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Bihar 2019', width=600, height=300)


</code>
"
2009,temporal_aggregation,Show the monthly average PM2.5 for Brajrajnagar in 2024 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Brajrajnagar') & (data['Timestamp'].dt.year == 2024)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Brajrajnagar 2024', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Brajrajnagar') & (data['Timestamp'].dt.year == 2024)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Brajrajnagar 2024', width=450, height=280)


</code>
"
2010,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Delhi, Manipur, and Himachal Pradesh from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Delhi', 'Manipur', 'Himachal Pradesh']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Delhi vs Manipur vs Himachal Pradesh', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Delhi', 'Manipur', 'Himachal Pradesh']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Delhi vs Manipur vs Himachal Pradesh', width=550, height=320)
     return chart


</code>
"
2011,temporal_aggregation,Show the monthly average PM2.5 for Ooty in 2024 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Ooty') & (data['Timestamp'].dt.year == 2024)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Ooty 2024', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Ooty') & (data['Timestamp'].dt.year == 2024)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Ooty 2024', width=450, height=280)


</code>
"
2012,temporal_aggregation,Show a monthly bar chart of the number of days Chhattisgarh exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2024.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Chhattisgarh') & (data['Timestamp'].dt.year == 2024)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Chhattisgarh Exceeded WHO PM2.5 Guideline per Month – 2024', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Chhattisgarh') & (data['Timestamp'].dt.year == 2024)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Chhattisgarh Exceeded WHO PM2.5 Guideline per Month – 2024', width=500, height=300)
     return chart


</code>
"
2013,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Rajasthan stations in 2024, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Rajasthan') & (data['Timestamp'].dt.year == 2024)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Rajasthan Stations 2024', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Rajasthan') & (data['Timestamp'].dt.year == 2024)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Rajasthan Stations 2024', width=450, height=350)


</code>
"
2014,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Coimbatore, Nagaon, and Kolhapur in 2020 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Coimbatore', 'Nagaon', 'Kolhapur']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2020)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Coimbatore vs Nagaon vs Kolhapur – 2020', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Coimbatore', 'Nagaon', 'Kolhapur']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2020)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Coimbatore vs Nagaon vs Kolhapur – 2020', width=550, height=320)
     return chart


</code>
"
2015,spatio_temporal_aggregation,"Create a faceted bar chart showing top 14 states by average PM2.5 per year for 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year.isin([2021,2022,2023,2024])].copy()
    df['Year'] = df['Timestamp'].dt.year
    agg = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    top = agg.groupby('Year').apply(lambda x: x.nlargest(14,'PM2.5')).reset_index(drop=True)
    chart = alt.Chart(top).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Avg PM2.5'),
        y=alt.Y('state:N', sort='-x'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).facet(facet='Year:O', columns=2).properties(title='Top 14 States by PM2.5 per Year')
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year.isin([2021,2022,2023,2024])].copy()
     df['Year'] = df['Timestamp'].dt.year
     agg = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     top = agg.groupby('Year').apply(lambda x: x.nlargest(14,'PM2.5')).reset_index(drop=True)
     chart = alt.Chart(top).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Avg PM2.5'),
         y=alt.Y('state:N', sort='-x'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).facet(facet='Year:O', columns=2).properties(title='Top 14 States by PM2.5 per Year')
     return chart


</code>
"
2016,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Odisha stations in 2019, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Odisha') & (data['Timestamp'].dt.year == 2019)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Odisha Stations 2019', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Odisha') & (data['Timestamp'].dt.year == 2019)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Odisha Stations 2019', width=450, height=350)


</code>
"
2017,temporal_aggregation,Plot the weekly average PM2.5 for Jind in 2023 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Jind') & (data['Timestamp'].dt.year == 2023)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Jind 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Jind') & (data['Timestamp'].dt.year == 2023)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Jind 2023', width=600, height=300)
     return chart


</code>
"
2018,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Jammu and Kashmir, Rajasthan, and Meghalaya across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Jammu and Kashmir', 'Rajasthan', 'Meghalaya']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Jammu and Kashmir', 'Rajasthan', 'Meghalaya']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
2019,temporal_aggregation,Show the monthly average PM2.5 for Hanumangarh in 2024 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Hanumangarh') & (data['Timestamp'].dt.year == 2024)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Hanumangarh 2024', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Hanumangarh') & (data['Timestamp'].dt.year == 2024)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Hanumangarh 2024', width=450, height=280)


</code>
"
2020,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Rajasthan, Jammu and Kashmir, and Maharashtra in 2018 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Rajasthan', 'Jammu and Kashmir', 'Maharashtra']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2018)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Rajasthan, Jammu and Kashmir, UP – 2018', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Rajasthan', 'Jammu and Kashmir', 'Maharashtra']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2018)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Rajasthan, Jammu and Kashmir, UP – 2018', width=550, height=320)
     return chart


</code>
"
2021,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Amritsar, Khanna, and Tonk in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Amritsar', 'Khanna', 'Tonk']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Amritsar vs Khanna vs Tonk – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Amritsar', 'Khanna', 'Tonk']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Amritsar vs Khanna vs Tonk – 2022', width=550, height=320)
     return chart


</code>
"
2022,temporal_aggregation,Show the monthly average PM10 trend for Kota from 2017 to 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Kota'].copy()
    df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Kota (2017–2022)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Kota'].copy()
     df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Kota (2017–2022)', width=600, height=300)
     return chart


</code>
"
2023,specific_pattern,Plot the rolling 30-day average PM2.5 for Haryana in 2023 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Haryana') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Haryana 2023', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Haryana') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Haryana 2023', width=600, height=300)


</code>
"
2024,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Chamarajanagar, Chittoor, and Amritsar in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Chamarajanagar', 'Chittoor', 'Amritsar']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Chamarajanagar vs Chittoor vs Amritsar – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Chamarajanagar', 'Chittoor', 'Amritsar']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Chamarajanagar vs Chittoor vs Amritsar – 2022', width=550, height=320)
     return chart


</code>
"
2025,temporal_aggregation,Show the monthly average PM2.5 for Belgaum in 2024 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Belgaum') & (data['Timestamp'].dt.year == 2024)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Belgaum 2024', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Belgaum') & (data['Timestamp'].dt.year == 2024)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Belgaum 2024', width=450, height=280)


</code>
"
2026,specific_pattern,Show a cumulative area chart of PM2.5 readings for Purnia across 2023.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Purnia') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Purnia 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Purnia') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Purnia 2023', width=600, height=300)
     return chart


</code>
"
2027,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Haryana, Gujarat, and Tripura across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Haryana', 'Gujarat', 'Tripura']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Haryana', 'Gujarat', 'Tripura']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
2028,temporal_aggregation,Show the monthly average PM2.5 for Cuddalore in 2024 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Cuddalore') & (data['Timestamp'].dt.year == 2024)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Cuddalore 2024', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Cuddalore') & (data['Timestamp'].dt.year == 2024)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Cuddalore 2024', width=450, height=280)


</code>
"
2029,temporal_aggregation,Plot the weekly average PM2.5 for Eloor in 2019 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Eloor') & (data['Timestamp'].dt.year == 2019)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Eloor 2019', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Eloor') & (data['Timestamp'].dt.year == 2019)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Eloor 2019', width=600, height=300)
     return chart


</code>
"
2030,temporal_aggregation,Show the monthly average PM10 trend for Begusarai from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Begusarai'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Begusarai (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Begusarai'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Begusarai (2019–2024)', width=600, height=300)
     return chart


</code>
"
2031,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Puducherry, Himachal Pradesh, and Delhi from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Puducherry', 'Himachal Pradesh', 'Delhi']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Puducherry vs Himachal Pradesh vs Delhi', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Puducherry', 'Himachal Pradesh', 'Delhi']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Puducherry vs Himachal Pradesh vs Delhi', width=550, height=320)
     return chart


</code>
"
2032,temporal_aggregation,Show a monthly bar chart of the number of days Haryana exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2021.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Haryana') & (data['Timestamp'].dt.year == 2021)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Haryana Exceeded WHO PM2.5 Guideline per Month – 2021', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Haryana') & (data['Timestamp'].dt.year == 2021)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Haryana Exceeded WHO PM2.5 Guideline per Month – 2021', width=500, height=300)
     return chart


</code>
"
2033,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Uttarakhand, Sikkim, and Delhi from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Uttarakhand', 'Sikkim', 'Delhi']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Uttarakhand vs Sikkim vs Delhi', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Uttarakhand', 'Sikkim', 'Delhi']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Uttarakhand vs Sikkim vs Delhi', width=550, height=320)
     return chart


</code>
"
2034,temporal_aggregation,Plot the weekly average PM2.5 for Nalbari in 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Nalbari') & (data['Timestamp'].dt.year == 2024)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Nalbari 2024', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Nalbari') & (data['Timestamp'].dt.year == 2024)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Nalbari 2024', width=600, height=300)
     return chart


</code>
"
2035,temporal_aggregation,Show the monthly average PM2.5 for Bhiwani in 2018 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Bhiwani') & (data['Timestamp'].dt.year == 2018)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Bhiwani 2018', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Bhiwani') & (data['Timestamp'].dt.year == 2018)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Bhiwani 2018', width=450, height=280)


</code>
"
2036,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Gujarat, Assam, and Punjab across 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Gujarat', 'Assam', 'Punjab']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Gujarat', 'Assam', 'Punjab']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
     return chart


</code>
"
2037,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Andhra Pradesh, Mizoram, and Chandigarh in 2018 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Andhra Pradesh', 'Mizoram', 'Chandigarh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2018)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Andhra Pradesh, Mizoram, UP – 2018', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Andhra Pradesh', 'Mizoram', 'Chandigarh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2018)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Andhra Pradesh, Mizoram, UP – 2018', width=550, height=320)
     return chart


</code>
"
2038,temporal_aggregation,Show the monthly average PM10 trend for Aurangabad from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Aurangabad'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Aurangabad (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Aurangabad'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Aurangabad (2019–2024)', width=600, height=300)
     return chart


</code>
"
2039,temporal_aggregation,Plot the weekly average PM2.5 for Khurja in 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Khurja') & (data['Timestamp'].dt.year == 2024)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Khurja 2024', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Khurja') & (data['Timestamp'].dt.year == 2024)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Khurja 2024', width=600, height=300)
     return chart


</code>
"
2040,temporal_aggregation,Show a monthly bar chart of the number of days Chhattisgarh exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2021.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Chhattisgarh') & (data['Timestamp'].dt.year == 2021)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Chhattisgarh Exceeded WHO PM2.5 Guideline per Month – 2021', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Chhattisgarh') & (data['Timestamp'].dt.year == 2021)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Chhattisgarh Exceeded WHO PM2.5 Guideline per Month – 2021', width=500, height=300)
     return chart


</code>
"
2041,spatio_temporal_aggregation,Show a heatmap of average PM2.5 for the top 7 most polluted states by month for 2017.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    top8 = data.groupby('state')['PM2.5'].mean().nlargest(7).index.tolist()
    df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2017)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_rect().encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('state:N', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
        tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Heatmap – Top 7 Polluted States by Month (2017)', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     top8 = data.groupby('state')['PM2.5'].mean().nlargest(7).index.tolist()
     df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2017)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_rect().encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('state:N', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
         tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Heatmap – Top 7 Polluted States by Month (2017)', width=500, height=300)
     return chart


</code>
"
2042,spatial_aggregation,"Show the top 8 states by average PM10 in 2021 as a bar chart, sorted in descending order.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2021]
    df = df.groupby('state')['PM10'].mean().reset_index().dropna()
    df = df.nlargest(8, 'PM10')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Top 8 States by Average PM10 in 2021', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2021]
     df = df.groupby('state')['PM10'].mean().reset_index().dropna()
     df = df.nlargest(8, 'PM10')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Top 8 States by Average PM10 in 2021', width=500, height=300)
     return chart


</code>
"
2043,temporal_aggregation,Show a monthly bar chart of the number of days Andhra Pradesh exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2017.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Andhra Pradesh') & (data['Timestamp'].dt.year == 2017)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Andhra Pradesh Exceeded WHO PM2.5 Guideline per Month – 2017', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Andhra Pradesh') & (data['Timestamp'].dt.year == 2017)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Andhra Pradesh Exceeded WHO PM2.5 Guideline per Month – 2017', width=500, height=300)
     return chart


</code>
"
2044,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Assam, Bihar, and Kerala from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Assam', 'Bihar', 'Kerala']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Assam vs Bihar vs Kerala', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Assam', 'Bihar', 'Kerala']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Assam vs Bihar vs Kerala', width=550, height=320)
     return chart


</code>
"
2045,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Muzaffarpur, Ujjain, and Latur in 2018 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Muzaffarpur', 'Ujjain', 'Latur']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2018)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Muzaffarpur vs Ujjain vs Latur – 2018', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Muzaffarpur', 'Ujjain', 'Latur']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2018)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Muzaffarpur vs Ujjain vs Latur – 2018', width=550, height=320)
     return chart


</code>
"
2046,temporal_aggregation,Show the monthly average PM10 trend for Hosur from 2017 to 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Hosur'].copy()
    df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Hosur (2017–2022)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Hosur'].copy()
     df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Hosur (2017–2022)', width=600, height=300)
     return chart


</code>
"
2047,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Chhattisgarh, Haryana, and Rajasthan in 2018 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Chhattisgarh', 'Haryana', 'Rajasthan']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2018)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Chhattisgarh, Haryana, UP – 2018', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Chhattisgarh', 'Haryana', 'Rajasthan']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2018)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Chhattisgarh, Haryana, UP – 2018', width=550, height=320)
     return chart


</code>
"
2048,spatio_temporal_aggregation,Show a heatmap of average PM2.5 for the top 7 most polluted states by month for 2023.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    top8 = data.groupby('state')['PM2.5'].mean().nlargest(7).index.tolist()
    df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_rect().encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('state:N', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
        tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Heatmap – Top 7 Polluted States by Month (2023)', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     top8 = data.groupby('state')['PM2.5'].mean().nlargest(7).index.tolist()
     df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_rect().encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('state:N', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
         tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Heatmap – Top 7 Polluted States by Month (2023)', width=500, height=300)
     return chart


</code>
"
2049,temporal_aggregation,Show the monthly average PM2.5 for Alwar in 2018 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Alwar') & (data['Timestamp'].dt.year == 2018)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Alwar 2018', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Alwar') & (data['Timestamp'].dt.year == 2018)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Alwar 2018', width=450, height=280)


</code>
"
2050,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Jammu and Kashmir, Karnataka, and Andhra Pradesh in 2018 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Jammu and Kashmir', 'Karnataka', 'Andhra Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2018)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Jammu and Kashmir, Karnataka, UP – 2018', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Jammu and Kashmir', 'Karnataka', 'Andhra Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2018)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Jammu and Kashmir, Karnataka, UP – 2018', width=550, height=320)
     return chart


</code>
"
2051,temporal_aggregation,Show the monthly average PM2.5 for Panchkula in 2023 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Panchkula') & (data['Timestamp'].dt.year == 2023)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Panchkula 2023', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Panchkula') & (data['Timestamp'].dt.year == 2023)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Panchkula 2023', width=450, height=280)


</code>
"
2052,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Karur, Byrnihat, and Rajamahendravaram in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Karur', 'Byrnihat', 'Rajamahendravaram']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Karur vs Byrnihat vs Rajamahendravaram – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Karur', 'Byrnihat', 'Rajamahendravaram']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Karur vs Byrnihat vs Rajamahendravaram – 2022', width=550, height=320)
     return chart


</code>
"
2053,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Rohtak, Vellore, and Singrauli in 2019 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Rohtak', 'Vellore', 'Singrauli']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Rohtak vs Vellore vs Singrauli – 2019', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Rohtak', 'Vellore', 'Singrauli']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Rohtak vs Vellore vs Singrauli – 2019', width=550, height=320)
     return chart


</code>
"
2054,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for West Bengal, Chandigarh, and Meghalaya across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['West Bengal', 'Chandigarh', 'Meghalaya']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['West Bengal', 'Chandigarh', 'Meghalaya']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
2055,specific_pattern,Plot the rolling 30-day average PM2.5 for Sikkim in 2018 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Sikkim') & (data['Timestamp'].dt.year == 2018)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Sikkim 2018', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Sikkim') & (data['Timestamp'].dt.year == 2018)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Sikkim 2018', width=600, height=300)


</code>
"
2056,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Punjab, Manipur, and West Bengal from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Punjab', 'Manipur', 'West Bengal']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Punjab vs Manipur vs West Bengal', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Punjab', 'Manipur', 'West Bengal']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Punjab vs Manipur vs West Bengal', width=550, height=320)
     return chart


</code>
"
2057,temporal_aggregation,Show a monthly bar chart of the number of days Odisha exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2023.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Odisha') & (data['Timestamp'].dt.year == 2023)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Odisha Exceeded WHO PM2.5 Guideline per Month – 2023', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Odisha') & (data['Timestamp'].dt.year == 2023)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Odisha Exceeded WHO PM2.5 Guideline per Month – 2023', width=500, height=300)
     return chart


</code>
"
2058,temporal_aggregation,Show the monthly average PM2.5 for Balasore in 2019 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Balasore') & (data['Timestamp'].dt.year == 2019)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Balasore 2019', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Balasore') & (data['Timestamp'].dt.year == 2019)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Balasore 2019', width=450, height=280)


</code>
"
2059,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Guwahati, Korba, and Virar in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Guwahati', 'Korba', 'Virar']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Guwahati vs Korba vs Virar – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Guwahati', 'Korba', 'Virar']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Guwahati vs Korba vs Virar – 2023', width=550, height=320)
     return chart


</code>
"
2060,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Chhattisgarh, Tamil Nadu, and Odisha in 2021 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Chhattisgarh', 'Tamil Nadu', 'Odisha']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Chhattisgarh, Tamil Nadu, UP – 2021', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Chhattisgarh', 'Tamil Nadu', 'Odisha']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Chhattisgarh, Tamil Nadu, UP – 2021', width=550, height=320)
     return chart


</code>
"
2061,temporal_aggregation,Show the monthly average PM2.5 for Meerut in 2018 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Meerut') & (data['Timestamp'].dt.year == 2018)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Meerut 2018', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Meerut') & (data['Timestamp'].dt.year == 2018)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Meerut 2018', width=450, height=280)


</code>
"
2062,specific_pattern,Show a cumulative area chart of PM2.5 readings for Singrauli across 2024.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Singrauli') & (data['Timestamp'].dt.year == 2024)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Singrauli 2024', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Singrauli') & (data['Timestamp'].dt.year == 2024)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Singrauli 2024', width=600, height=300)
     return chart


</code>
"
2063,specific_pattern,Show a cumulative area chart of PM2.5 readings for Pithampur across 2021.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Pithampur') & (data['Timestamp'].dt.year == 2021)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Pithampur 2021', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Pithampur') & (data['Timestamp'].dt.year == 2021)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Pithampur 2021', width=600, height=300)
     return chart


</code>
"
2064,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Mizoram, Puducherry, and Kerala from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Mizoram', 'Puducherry', 'Kerala']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Mizoram vs Puducherry vs Kerala', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Mizoram', 'Puducherry', 'Kerala']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Mizoram vs Puducherry vs Kerala', width=550, height=320)
     return chart


</code>
"
2065,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Puducherry, Mizoram, and Punjab in 2019 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Puducherry', 'Mizoram', 'Punjab']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Puducherry, Mizoram, UP – 2019', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Puducherry', 'Mizoram', 'Punjab']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Puducherry, Mizoram, UP – 2019', width=550, height=320)
     return chart


</code>
"
2066,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Delhi, Chhattisgarh, and Mizoram across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Delhi', 'Chhattisgarh', 'Mizoram']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Delhi', 'Chhattisgarh', 'Mizoram']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
2067,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Tamil Nadu, Madhya Pradesh, and Arunachal Pradesh in 2019 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Tamil Nadu', 'Madhya Pradesh', 'Arunachal Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Tamil Nadu, Madhya Pradesh, UP – 2019', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Tamil Nadu', 'Madhya Pradesh', 'Arunachal Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Tamil Nadu, Madhya Pradesh, UP – 2019', width=550, height=320)
     return chart


</code>
"
2068,specific_pattern,Show a cumulative area chart of PM2.5 readings for Kadapa across 2023.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Kadapa') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Kadapa 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Kadapa') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Kadapa 2023', width=600, height=300)
     return chart


</code>
"
2069,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Jammu and Kashmir, Himachal Pradesh, and Telangana from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Jammu and Kashmir', 'Himachal Pradesh', 'Telangana']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Jammu and Kashmir vs Himachal Pradesh vs Telangana', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Jammu and Kashmir', 'Himachal Pradesh', 'Telangana']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Jammu and Kashmir vs Himachal Pradesh vs Telangana', width=550, height=320)
     return chart


</code>
"
2070,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Malegaon, Meerut, and Siliguri in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Malegaon', 'Meerut', 'Siliguri']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Malegaon vs Meerut vs Siliguri – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Malegaon', 'Meerut', 'Siliguri']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Malegaon vs Meerut vs Siliguri – 2024', width=550, height=320)
     return chart


</code>
"
2071,specific_pattern,Show a cumulative area chart of PM2.5 readings for Ghaziabad across 2017.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Ghaziabad') & (data['Timestamp'].dt.year == 2017)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Ghaziabad 2017', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Ghaziabad') & (data['Timestamp'].dt.year == 2017)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Ghaziabad 2017', width=600, height=300)
     return chart


</code>
"
2072,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Mira-Bhayandar, Tumakuru, and Mandideep in 2018 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Mira-Bhayandar', 'Tumakuru', 'Mandideep']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2018)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Mira-Bhayandar vs Tumakuru vs Mandideep – 2018', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Mira-Bhayandar', 'Tumakuru', 'Mandideep']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2018)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Mira-Bhayandar vs Tumakuru vs Mandideep – 2018', width=550, height=320)
     return chart


</code>
"
2073,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Puducherry, Chandigarh, and Haryana in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Puducherry', 'Chandigarh', 'Haryana']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Puducherry, Chandigarh, UP – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Puducherry', 'Chandigarh', 'Haryana']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Puducherry, Chandigarh, UP – 2022', width=550, height=320)
     return chart


</code>
"
2074,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Madhya Pradesh, Tamil Nadu, and Odisha from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Madhya Pradesh', 'Tamil Nadu', 'Odisha']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Madhya Pradesh vs Tamil Nadu vs Odisha', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Madhya Pradesh', 'Tamil Nadu', 'Odisha']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Madhya Pradesh vs Tamil Nadu vs Odisha', width=550, height=320)
     return chart


</code>
"
2075,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Pune, Kadapa, and Muzaffarnagar in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Pune', 'Kadapa', 'Muzaffarnagar']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Pune vs Kadapa vs Muzaffarnagar – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Pune', 'Kadapa', 'Muzaffarnagar']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Pune vs Kadapa vs Muzaffarnagar – 2023', width=550, height=320)
     return chart


</code>
"
2076,specific_pattern,Show a cumulative area chart of PM2.5 readings for Vijayawada across 2023.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Vijayawada') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Vijayawada 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Vijayawada') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Vijayawada 2023', width=600, height=300)
     return chart


</code>
"
2077,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Madhya Pradesh, Mizoram, and Haryana from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Madhya Pradesh', 'Mizoram', 'Haryana']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Madhya Pradesh vs Mizoram vs Haryana', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Madhya Pradesh', 'Mizoram', 'Haryana']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Madhya Pradesh vs Mizoram vs Haryana', width=550, height=320)
     return chart


</code>
"
2078,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for West Bengal, Andhra Pradesh, and Chhattisgarh across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['West Bengal', 'Andhra Pradesh', 'Chhattisgarh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['West Bengal', 'Andhra Pradesh', 'Chhattisgarh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
2079,temporal_aggregation,Show the monthly average PM10 trend for Hanumangarh from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Hanumangarh'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Hanumangarh (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Hanumangarh'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Hanumangarh (2019–2024)', width=600, height=300)
     return chart


</code>
"
2080,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Mizoram, Uttar Pradesh, and Tamil Nadu from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Mizoram', 'Uttar Pradesh', 'Tamil Nadu']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Mizoram vs Uttar Pradesh vs Tamil Nadu', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Mizoram', 'Uttar Pradesh', 'Tamil Nadu']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Mizoram vs Uttar Pradesh vs Tamil Nadu', width=550, height=320)
     return chart


</code>
"
2081,spatial_aggregation,Plot the top 5 states by average PM2.5 in 2022 as a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2022]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nlargest(5, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 5 States by Average PM2.5 in 2022', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2022]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nlargest(5, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 5 States by Average PM2.5 in 2022', width=500, height=300)
     return chart


</code>
"
2082,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Chhattisgarh, Sikkim, and Telangana in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Chhattisgarh', 'Sikkim', 'Telangana']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Chhattisgarh, Sikkim, UP – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Chhattisgarh', 'Sikkim', 'Telangana']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Chhattisgarh, Sikkim, UP – 2022', width=550, height=320)
     return chart


</code>
"
2083,specific_pattern,Show a cumulative area chart of PM2.5 readings for Nashik across 2023.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Nashik') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Nashik 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Nashik') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Nashik 2023', width=600, height=300)
     return chart


</code>
"
2084,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Chhattisgarh, Chhattisgarh, and Assam across 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Chhattisgarh', 'Chhattisgarh', 'Assam']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Chhattisgarh', 'Chhattisgarh', 'Assam']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
     return chart


</code>
"
2085,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Meerut, Howrah, and Gorakhpur in 2020 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Meerut', 'Howrah', 'Gorakhpur']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2020)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Meerut vs Howrah vs Gorakhpur – 2020', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Meerut', 'Howrah', 'Gorakhpur']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2020)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Meerut vs Howrah vs Gorakhpur – 2020', width=550, height=320)
     return chart


</code>
"
2086,specific_pattern,Plot the rolling 30-day average PM2.5 for Mizoram in 2018 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Mizoram') & (data['Timestamp'].dt.year == 2018)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Mizoram 2018', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Mizoram') & (data['Timestamp'].dt.year == 2018)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Mizoram 2018', width=600, height=300)


</code>
"
2087,temporal_aggregation,Show the monthly average PM2.5 for Dausa in 2022 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Dausa') & (data['Timestamp'].dt.year == 2022)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Dausa 2022', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Dausa') & (data['Timestamp'].dt.year == 2022)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Dausa 2022', width=450, height=280)


</code>
"
2088,temporal_aggregation,Show the monthly average PM10 trend for Ghaziabad from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Ghaziabad'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Ghaziabad (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Ghaziabad'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Ghaziabad (2019–2024)', width=600, height=300)
     return chart


</code>
"
2089,specific_pattern,Plot the rolling 30-day average PM2.5 for Arunachal Pradesh in 2023 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Arunachal Pradesh') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Arunachal Pradesh 2023', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Arunachal Pradesh') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Arunachal Pradesh 2023', width=600, height=300)


</code>
"
2090,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Manipur, Puducherry, and Jharkhand across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Manipur', 'Puducherry', 'Jharkhand']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Manipur', 'Puducherry', 'Jharkhand']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
2091,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Punjab, Assam, and Jammu and Kashmir from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Punjab', 'Assam', 'Jammu and Kashmir']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Punjab vs Assam vs Jammu and Kashmir', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Punjab', 'Assam', 'Jammu and Kashmir']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Punjab vs Assam vs Jammu and Kashmir', width=550, height=320)
     return chart


</code>
"
2092,temporal_aggregation,Plot the weekly average PM2.5 for Gurugram in 2020 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Gurugram') & (data['Timestamp'].dt.year == 2020)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Gurugram 2020', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Gurugram') & (data['Timestamp'].dt.year == 2020)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Gurugram 2020', width=600, height=300)
     return chart


</code>
"
2093,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Jharkhand, Telangana, and Uttarakhand from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Jharkhand', 'Telangana', 'Uttarakhand']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Jharkhand vs Telangana vs Uttarakhand', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Jharkhand', 'Telangana', 'Uttarakhand']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Jharkhand vs Telangana vs Uttarakhand', width=550, height=320)
     return chart


</code>
"
2094,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Kannur, Gummidipoondi, and Koppal in 2020 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Kannur', 'Gummidipoondi', 'Koppal']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2020)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Kannur vs Gummidipoondi vs Koppal – 2020', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Kannur', 'Gummidipoondi', 'Koppal']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2020)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Kannur vs Gummidipoondi vs Koppal – 2020', width=550, height=320)
     return chart


</code>
"
2095,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Andhra Pradesh stations in 2023, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Andhra Pradesh') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Andhra Pradesh Stations 2023', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Andhra Pradesh') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Andhra Pradesh Stations 2023', width=450, height=350)


</code>
"
2096,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Kerala stations in 2024, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Kerala') & (data['Timestamp'].dt.year == 2024)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Kerala Stations 2024', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Kerala') & (data['Timestamp'].dt.year == 2024)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Kerala Stations 2024', width=450, height=350)


</code>
"
2097,temporal_aggregation,Show the monthly average PM2.5 for Singrauli in 2024 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Singrauli') & (data['Timestamp'].dt.year == 2024)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Singrauli 2024', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Singrauli') & (data['Timestamp'].dt.year == 2024)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Singrauli 2024', width=450, height=280)


</code>
"
2098,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Badlapur, Ooty, and Ajmer in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Badlapur', 'Ooty', 'Ajmer']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Badlapur vs Ooty vs Ajmer – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Badlapur', 'Ooty', 'Ajmer']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Badlapur vs Ooty vs Ajmer – 2022', width=550, height=320)
     return chart


</code>
"
2099,specific_pattern,Show a cumulative area chart of PM2.5 readings for Ramanathapuram across 2024.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Ramanathapuram') & (data['Timestamp'].dt.year == 2024)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Ramanathapuram 2024', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Ramanathapuram') & (data['Timestamp'].dt.year == 2024)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Ramanathapuram 2024', width=600, height=300)
     return chart


</code>
"
2100,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Udaipur, Kashipur, and Pali in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Udaipur', 'Kashipur', 'Pali']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Udaipur vs Kashipur vs Pali – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Udaipur', 'Kashipur', 'Pali']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Udaipur vs Kashipur vs Pali – 2023', width=550, height=320)
     return chart


</code>
"
2101,temporal_aggregation,Show the monthly average PM2.5 for Virar in 2017 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Virar') & (data['Timestamp'].dt.year == 2017)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Virar 2017', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Virar') & (data['Timestamp'].dt.year == 2017)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Virar 2017', width=450, height=280)


</code>
"
2102,specific_pattern,Show a cumulative area chart of PM2.5 readings for Bathinda across 2024.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Bathinda') & (data['Timestamp'].dt.year == 2024)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Bathinda 2024', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Bathinda') & (data['Timestamp'].dt.year == 2024)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Bathinda 2024', width=600, height=300)
     return chart


</code>
"
2103,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Jharkhand, Nagaland, and Madhya Pradesh in 2021 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Jharkhand', 'Nagaland', 'Madhya Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Jharkhand, Nagaland, UP – 2021', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Jharkhand', 'Nagaland', 'Madhya Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Jharkhand, Nagaland, UP – 2021', width=550, height=320)
     return chart


</code>
"
2104,temporal_aggregation,Show the monthly average PM2.5 for Gwalior in 2024 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Gwalior') & (data['Timestamp'].dt.year == 2024)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Gwalior 2024', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Gwalior') & (data['Timestamp'].dt.year == 2024)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Gwalior 2024', width=450, height=280)


</code>
"
2105,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Himachal Pradesh, Manipur, and Meghalaya in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Himachal Pradesh', 'Manipur', 'Meghalaya']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Himachal Pradesh, Manipur, UP – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Himachal Pradesh', 'Manipur', 'Meghalaya']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Himachal Pradesh, Manipur, UP – 2022', width=550, height=320)
     return chart


</code>
"
2106,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Sikkim, Arunachal Pradesh, and Odisha from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Sikkim', 'Arunachal Pradesh', 'Odisha']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Sikkim vs Arunachal Pradesh vs Odisha', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Sikkim', 'Arunachal Pradesh', 'Odisha']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Sikkim vs Arunachal Pradesh vs Odisha', width=550, height=320)
     return chart


</code>
"
2107,spatio_temporal_aggregation,Show a heatmap of average PM2.5 for the top 7 most polluted states by month for 2018.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    top8 = data.groupby('state')['PM2.5'].mean().nlargest(7).index.tolist()
    df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2018)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_rect().encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('state:N', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
        tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Heatmap – Top 7 Polluted States by Month (2018)', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     top8 = data.groupby('state')['PM2.5'].mean().nlargest(7).index.tolist()
     df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2018)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_rect().encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('state:N', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
         tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Heatmap – Top 7 Polluted States by Month (2018)', width=500, height=300)
     return chart


</code>
"
2108,temporal_aggregation,Show a monthly bar chart of the number of days Rajasthan exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2020.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Rajasthan') & (data['Timestamp'].dt.year == 2020)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Rajasthan Exceeded WHO PM2.5 Guideline per Month – 2020', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Rajasthan') & (data['Timestamp'].dt.year == 2020)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Rajasthan Exceeded WHO PM2.5 Guideline per Month – 2020', width=500, height=300)
     return chart


</code>
"
2109,spatial_aggregation,"Show the top 9 states by average PM10 in 2021 as a bar chart, sorted in descending order.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2021]
    df = df.groupby('state')['PM10'].mean().reset_index().dropna()
    df = df.nlargest(9, 'PM10')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Top 9 States by Average PM10 in 2021', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2021]
     df = df.groupby('state')['PM10'].mean().reset_index().dropna()
     df = df.nlargest(9, 'PM10')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Top 9 States by Average PM10 in 2021', width=500, height=300)
     return chart


</code>
"
2110,temporal_aggregation,Plot the weekly average PM2.5 for Pithampur in 2020 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Pithampur') & (data['Timestamp'].dt.year == 2020)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Pithampur 2020', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Pithampur') & (data['Timestamp'].dt.year == 2020)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Pithampur 2020', width=600, height=300)
     return chart


</code>
"
2111,temporal_aggregation,Plot the weekly average PM2.5 for Gadag in 2023 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Gadag') & (data['Timestamp'].dt.year == 2023)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Gadag 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Gadag') & (data['Timestamp'].dt.year == 2023)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Gadag 2023', width=600, height=300)
     return chart


</code>
"
2112,temporal_aggregation,Show the monthly average PM2.5 for Malegaon in 2018 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Malegaon') & (data['Timestamp'].dt.year == 2018)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Malegaon 2018', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Malegaon') & (data['Timestamp'].dt.year == 2018)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Malegaon 2018', width=450, height=280)


</code>
"
2113,temporal_aggregation,Show the monthly average PM2.5 for Pathardih in 2018 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Pathardih') & (data['Timestamp'].dt.year == 2018)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Pathardih 2018', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Pathardih') & (data['Timestamp'].dt.year == 2018)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Pathardih 2018', width=450, height=280)


</code>
"
2114,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Chandigarh, Rajasthan, and Sikkim in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Chandigarh', 'Rajasthan', 'Sikkim']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Chandigarh, Rajasthan, UP – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Chandigarh', 'Rajasthan', 'Sikkim']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Chandigarh, Rajasthan, UP – 2024', width=550, height=320)
     return chart


</code>
"
2115,temporal_aggregation,Show the monthly average PM2.5 for Damoh in 2023 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Damoh') & (data['Timestamp'].dt.year == 2023)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Damoh 2023', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Damoh') & (data['Timestamp'].dt.year == 2023)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Damoh 2023', width=450, height=280)


</code>
"
2116,temporal_aggregation,Show a monthly bar chart of the number of days Meghalaya exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2022.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Meghalaya') & (data['Timestamp'].dt.year == 2022)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Meghalaya Exceeded WHO PM2.5 Guideline per Month – 2022', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Meghalaya') & (data['Timestamp'].dt.year == 2022)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Meghalaya Exceeded WHO PM2.5 Guideline per Month – 2022', width=500, height=300)
     return chart


</code>
"
2117,temporal_aggregation,Show the monthly average PM10 trend for Noida from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Noida'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Noida (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Noida'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Noida (2019–2024)', width=600, height=300)
     return chart


</code>
"
2118,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Jharkhand stations in 2018, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Jharkhand') & (data['Timestamp'].dt.year == 2018)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Jharkhand Stations 2018', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Jharkhand') & (data['Timestamp'].dt.year == 2018)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Jharkhand Stations 2018', width=450, height=350)


</code>
"
2119,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Thrissur, Karur, and Thane in 2018 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Thrissur', 'Karur', 'Thane']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2018)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Thrissur vs Karur vs Thane – 2018', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Thrissur', 'Karur', 'Thane']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2018)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Thrissur vs Karur vs Thane – 2018', width=550, height=320)
     return chart


</code>
"
2120,temporal_aggregation,Show a monthly bar chart of the number of days Odisha exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2017.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Odisha') & (data['Timestamp'].dt.year == 2017)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Odisha Exceeded WHO PM2.5 Guideline per Month – 2017', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Odisha') & (data['Timestamp'].dt.year == 2017)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Odisha Exceeded WHO PM2.5 Guideline per Month – 2017', width=500, height=300)
     return chart


</code>
"
2121,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Haryana, Himachal Pradesh, and Madhya Pradesh in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Haryana', 'Himachal Pradesh', 'Madhya Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Haryana, Himachal Pradesh, UP – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Haryana', 'Himachal Pradesh', 'Madhya Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Haryana, Himachal Pradesh, UP – 2023', width=550, height=320)
     return chart


</code>
"
2122,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Arunachal Pradesh, Rajasthan, and Haryana across 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Arunachal Pradesh', 'Rajasthan', 'Haryana']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Arunachal Pradesh', 'Rajasthan', 'Haryana']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
     return chart


</code>
"
2123,temporal_aggregation,Show the monthly average PM2.5 for Nalbari in 2020 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Nalbari') & (data['Timestamp'].dt.year == 2020)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Nalbari 2020', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Nalbari') & (data['Timestamp'].dt.year == 2020)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Nalbari 2020', width=450, height=280)


</code>
"
2124,specific_pattern,Show a cumulative area chart of PM2.5 readings for Kanchipuram across 2023.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Kanchipuram') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Kanchipuram 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Kanchipuram') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Kanchipuram 2023', width=600, height=300)
     return chart


</code>
"
2125,specific_pattern,Show a cumulative area chart of PM2.5 readings for Maihar across 2021.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Maihar') & (data['Timestamp'].dt.year == 2021)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Maihar 2021', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Maihar') & (data['Timestamp'].dt.year == 2021)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Maihar 2021', width=600, height=300)
     return chart


</code>
"
2126,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Punjab, Chandigarh, and Jammu and Kashmir across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Punjab', 'Chandigarh', 'Jammu and Kashmir']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Punjab', 'Chandigarh', 'Jammu and Kashmir']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
2127,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Karwar, Sirohi, and Gandhinagar in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Karwar', 'Sirohi', 'Gandhinagar']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Karwar vs Sirohi vs Gandhinagar – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Karwar', 'Sirohi', 'Gandhinagar']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Karwar vs Sirohi vs Gandhinagar – 2022', width=550, height=320)
     return chart


</code>
"
2128,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Tripura, Assam, and Andhra Pradesh in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Tripura', 'Assam', 'Andhra Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Tripura, Assam, UP – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Tripura', 'Assam', 'Andhra Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Tripura, Assam, UP – 2024', width=550, height=320)
     return chart


</code>
"
2129,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Delhi, Punjab, and Arunachal Pradesh across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Delhi', 'Punjab', 'Arunachal Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Delhi', 'Punjab', 'Arunachal Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
2130,specific_pattern,Plot the rolling 30-day average PM2.5 for Tripura in 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Tripura') & (data['Timestamp'].dt.year == 2022)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Tripura 2022', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Tripura') & (data['Timestamp'].dt.year == 2022)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Tripura 2022', width=600, height=300)


</code>
"
2131,temporal_aggregation,Show the monthly average PM2.5 for Churu in 2019 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Churu') & (data['Timestamp'].dt.year == 2019)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Churu 2019', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Churu') & (data['Timestamp'].dt.year == 2019)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Churu 2019', width=450, height=280)


</code>
"
2132,specific_pattern,Show a cumulative area chart of PM2.5 readings for Bengaluru across 2017.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Bengaluru') & (data['Timestamp'].dt.year == 2017)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Bengaluru 2017', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Bengaluru') & (data['Timestamp'].dt.year == 2017)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Bengaluru 2017', width=600, height=300)
     return chart


</code>
"
2133,temporal_aggregation,Show the monthly average PM2.5 for Kurukshetra  in 2018 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Kurukshetra ') & (data['Timestamp'].dt.year == 2018)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Kurukshetra  2018', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Kurukshetra ') & (data['Timestamp'].dt.year == 2018)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Kurukshetra  2018', width=450, height=280)


</code>
"
2134,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Chhattisgarh, Odisha, and Karnataka from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Chhattisgarh', 'Odisha', 'Karnataka']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Chhattisgarh vs Odisha vs Karnataka', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Chhattisgarh', 'Odisha', 'Karnataka']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Chhattisgarh vs Odisha vs Karnataka', width=550, height=320)
     return chart


</code>
"
2135,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Telangana, Rajasthan, and Jharkhand across 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Telangana', 'Rajasthan', 'Jharkhand']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Telangana', 'Rajasthan', 'Jharkhand']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
     return chart


</code>
"
2136,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Odisha, Andhra Pradesh, and Uttar Pradesh in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Odisha', 'Andhra Pradesh', 'Uttar Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Odisha, Andhra Pradesh, UP – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Odisha', 'Andhra Pradesh', 'Uttar Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Odisha, Andhra Pradesh, UP – 2022', width=550, height=320)
     return chart


</code>
"
2137,specific_pattern,Plot the rolling 30-day average PM2.5 for Himachal Pradesh in 2019 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Himachal Pradesh') & (data['Timestamp'].dt.year == 2019)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Himachal Pradesh 2019', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Himachal Pradesh') & (data['Timestamp'].dt.year == 2019)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Himachal Pradesh 2019', width=600, height=300)


</code>
"
2138,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Puducherry, Uttar Pradesh, and Sikkim across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Puducherry', 'Uttar Pradesh', 'Sikkim']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Puducherry', 'Uttar Pradesh', 'Sikkim']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
2139,specific_pattern,Plot the rolling 30-day average PM2.5 for Jammu and Kashmir in 2017 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Jammu and Kashmir') & (data['Timestamp'].dt.year == 2017)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Jammu and Kashmir 2017', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Jammu and Kashmir') & (data['Timestamp'].dt.year == 2017)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Jammu and Kashmir 2017', width=600, height=300)


</code>
"
2140,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Mizoram, Arunachal Pradesh, and Chhattisgarh from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Mizoram', 'Arunachal Pradesh', 'Chhattisgarh']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Mizoram vs Arunachal Pradesh vs Chhattisgarh', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Mizoram', 'Arunachal Pradesh', 'Chhattisgarh']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Mizoram vs Arunachal Pradesh vs Chhattisgarh', width=550, height=320)
     return chart


</code>
"
2141,temporal_aggregation,Plot the weekly average PM2.5 for Amaravati in 2017 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Amaravati') & (data['Timestamp'].dt.year == 2017)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Amaravati 2017', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Amaravati') & (data['Timestamp'].dt.year == 2017)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Amaravati 2017', width=600, height=300)
     return chart


</code>
"
2142,temporal_aggregation,Show the monthly average PM2.5 for Solapur in 2017 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Solapur') & (data['Timestamp'].dt.year == 2017)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Solapur 2017', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Solapur') & (data['Timestamp'].dt.year == 2017)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Solapur 2017', width=450, height=280)


</code>
"
2143,temporal_aggregation,Show the monthly average PM2.5 for Sonipat in 2020 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Sonipat') & (data['Timestamp'].dt.year == 2020)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Sonipat 2020', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Sonipat') & (data['Timestamp'].dt.year == 2020)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Sonipat 2020', width=450, height=280)


</code>
"
2144,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for West Bengal, Chhattisgarh, and Tripura across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['West Bengal', 'Chhattisgarh', 'Tripura']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['West Bengal', 'Chhattisgarh', 'Tripura']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
2145,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Maharashtra, West Bengal, and Tamil Nadu in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Maharashtra', 'West Bengal', 'Tamil Nadu']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Maharashtra, West Bengal, UP – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Maharashtra', 'West Bengal', 'Tamil Nadu']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Maharashtra, West Bengal, UP – 2022', width=550, height=320)
     return chart


</code>
"
2146,temporal_aggregation,Plot the weekly average PM2.5 for Sirsa in 2020 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Sirsa') & (data['Timestamp'].dt.year == 2020)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Sirsa 2020', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Sirsa') & (data['Timestamp'].dt.year == 2020)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Sirsa 2020', width=600, height=300)
     return chart


</code>
"
2147,spatial_aggregation,Plot the top 15 states by average PM2.5 in 2019 as a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2019]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nlargest(15, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 15 States by Average PM2.5 in 2019', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2019]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nlargest(15, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 15 States by Average PM2.5 in 2019', width=500, height=300)
     return chart


</code>
"
2148,temporal_aggregation,Show the monthly average PM10 trend for Mandideep from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Mandideep'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Mandideep (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Mandideep'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Mandideep (2019–2024)', width=600, height=300)
     return chart


</code>
"
2149,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Punjab, Nagaland, and Mizoram from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Punjab', 'Nagaland', 'Mizoram']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Punjab vs Nagaland vs Mizoram', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Punjab', 'Nagaland', 'Mizoram']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Punjab vs Nagaland vs Mizoram', width=550, height=320)
     return chart


</code>
"
2150,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Gujarat, Telangana, and Maharashtra in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Gujarat', 'Telangana', 'Maharashtra']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Gujarat, Telangana, UP – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Gujarat', 'Telangana', 'Maharashtra']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Gujarat, Telangana, UP – 2024', width=550, height=320)
     return chart


</code>
"
2151,temporal_aggregation,Show the monthly average PM2.5 for Karauli in 2024 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Karauli') & (data['Timestamp'].dt.year == 2024)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Karauli 2024', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Karauli') & (data['Timestamp'].dt.year == 2024)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Karauli 2024', width=450, height=280)


</code>
"
2152,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Odisha, Jammu and Kashmir, and Andhra Pradesh from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Odisha', 'Jammu and Kashmir', 'Andhra Pradesh']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Odisha vs Jammu and Kashmir vs Andhra Pradesh', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Odisha', 'Jammu and Kashmir', 'Andhra Pradesh']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Odisha vs Jammu and Kashmir vs Andhra Pradesh', width=550, height=320)
     return chart


</code>
"
2153,temporal_aggregation,Show the monthly average PM2.5 for Firozabad in 2024 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Firozabad') & (data['Timestamp'].dt.year == 2024)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Firozabad 2024', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Firozabad') & (data['Timestamp'].dt.year == 2024)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Firozabad 2024', width=450, height=280)


</code>
"
2154,specific_pattern,Show a cumulative area chart of PM2.5 readings for Ajmer across 2024.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Ajmer') & (data['Timestamp'].dt.year == 2024)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Ajmer 2024', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Ajmer') & (data['Timestamp'].dt.year == 2024)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Ajmer 2024', width=600, height=300)
     return chart


</code>
"
2155,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Jharkhand stations in 2017, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Jharkhand') & (data['Timestamp'].dt.year == 2017)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Jharkhand Stations 2017', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Jharkhand') & (data['Timestamp'].dt.year == 2017)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Jharkhand Stations 2017', width=450, height=350)


</code>
"
2156,temporal_aggregation,Show the monthly average PM10 trend for Barmer from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Barmer'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Barmer (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Barmer'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Barmer (2019–2024)', width=600, height=300)
     return chart


</code>
"
2157,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Virudhunagar, Baran, and Jaipur in 2018 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Virudhunagar', 'Baran', 'Jaipur']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2018)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Virudhunagar vs Baran vs Jaipur – 2018', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Virudhunagar', 'Baran', 'Jaipur']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2018)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Virudhunagar vs Baran vs Jaipur – 2018', width=550, height=320)
     return chart


</code>
"
2158,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for West Bengal, Tripura, and Chandigarh from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['West Bengal', 'Tripura', 'Chandigarh']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: West Bengal vs Tripura vs Chandigarh', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['West Bengal', 'Tripura', 'Chandigarh']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: West Bengal vs Tripura vs Chandigarh', width=550, height=320)
     return chart


</code>
"
2159,temporal_aggregation,Show a monthly bar chart of the number of days Delhi exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2021.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Delhi') & (data['Timestamp'].dt.year == 2021)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Delhi Exceeded WHO PM2.5 Guideline per Month – 2021', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Delhi') & (data['Timestamp'].dt.year == 2021)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Delhi Exceeded WHO PM2.5 Guideline per Month – 2021', width=500, height=300)
     return chart


</code>
"
2160,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Ratlam, Katihar, and Chamarajanagar in 2020 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Ratlam', 'Katihar', 'Chamarajanagar']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2020)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Ratlam vs Katihar vs Chamarajanagar – 2020', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Ratlam', 'Katihar', 'Chamarajanagar']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2020)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Ratlam vs Katihar vs Chamarajanagar – 2020', width=550, height=320)
     return chart


</code>
"
2161,specific_pattern,Show a cumulative area chart of PM2.5 readings for Haldia across 2023.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Haldia') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Haldia 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Haldia') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Haldia 2023', width=600, height=300)
     return chart


</code>
"
2162,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Bihar, Nagaland, and Uttar Pradesh in 2017 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Bihar', 'Nagaland', 'Uttar Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2017)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Bihar, Nagaland, UP – 2017', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Bihar', 'Nagaland', 'Uttar Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2017)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Bihar, Nagaland, UP – 2017', width=550, height=320)
     return chart


</code>
"
2163,temporal_aggregation,Show the monthly average PM2.5 for Nagapattinam in 2020 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Nagapattinam') & (data['Timestamp'].dt.year == 2020)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Nagapattinam 2020', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Nagapattinam') & (data['Timestamp'].dt.year == 2020)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Nagapattinam 2020', width=450, height=280)


</code>
"
2164,spatial_aggregation,"Show the top 5 states by average PM10 in 2020 as a bar chart, sorted in descending order.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2020]
    df = df.groupby('state')['PM10'].mean().reset_index().dropna()
    df = df.nlargest(5, 'PM10')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Top 5 States by Average PM10 in 2020', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2020]
     df = df.groupby('state')['PM10'].mean().reset_index().dropna()
     df = df.nlargest(5, 'PM10')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Top 5 States by Average PM10 in 2020', width=500, height=300)
     return chart


</code>
"
2165,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Araria, Chamarajanagar, and Gummidipoondi in 2020 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Araria', 'Chamarajanagar', 'Gummidipoondi']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2020)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Araria vs Chamarajanagar vs Gummidipoondi – 2020', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Araria', 'Chamarajanagar', 'Gummidipoondi']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2020)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Araria vs Chamarajanagar vs Gummidipoondi – 2020', width=550, height=320)
     return chart


</code>
"
2166,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Mizoram, Tripura, and Mizoram in 2021 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Mizoram', 'Tripura', 'Mizoram']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Mizoram, Tripura, UP – 2021', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Mizoram', 'Tripura', 'Mizoram']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Mizoram, Tripura, UP – 2021', width=550, height=320)
     return chart


</code>
"
2167,specific_pattern,Show a cumulative area chart of PM2.5 readings for Kannur across 2022.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Kannur') & (data['Timestamp'].dt.year == 2022)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Kannur 2022', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Kannur') & (data['Timestamp'].dt.year == 2022)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Kannur 2022', width=600, height=300)
     return chart


</code>
"
2168,specific_pattern,Show a cumulative area chart of PM2.5 readings for Shivamogga across 2022.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Shivamogga') & (data['Timestamp'].dt.year == 2022)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Shivamogga 2022', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Shivamogga') & (data['Timestamp'].dt.year == 2022)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Shivamogga 2022', width=600, height=300)
     return chart


</code>
"
2169,temporal_aggregation,Plot the weekly average PM2.5 for Pali in 2017 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Pali') & (data['Timestamp'].dt.year == 2017)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Pali 2017', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Pali') & (data['Timestamp'].dt.year == 2017)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Pali 2017', width=600, height=300)
     return chart


</code>
"
2170,temporal_aggregation,Show the monthly average PM2.5 for Ramanathapuram in 2018 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Ramanathapuram') & (data['Timestamp'].dt.year == 2018)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Ramanathapuram 2018', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Ramanathapuram') & (data['Timestamp'].dt.year == 2018)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Ramanathapuram 2018', width=450, height=280)


</code>
"
2171,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Tamil Nadu, Punjab, and Gujarat across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Tamil Nadu', 'Punjab', 'Gujarat']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Tamil Nadu', 'Punjab', 'Gujarat']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
2172,specific_pattern,Plot the rolling 30-day average PM2.5 for Uttar Pradesh in 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Uttar Pradesh') & (data['Timestamp'].dt.year == 2024)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Uttar Pradesh 2024', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Uttar Pradesh') & (data['Timestamp'].dt.year == 2024)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Uttar Pradesh 2024', width=600, height=300)


</code>
"
2173,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Nagaland, Uttar Pradesh, and Haryana across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Nagaland', 'Uttar Pradesh', 'Haryana']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Nagaland', 'Uttar Pradesh', 'Haryana']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
2174,temporal_aggregation,Plot the weekly average PM2.5 for Ooty in 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Ooty') & (data['Timestamp'].dt.year == 2024)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Ooty 2024', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Ooty') & (data['Timestamp'].dt.year == 2024)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Ooty 2024', width=600, height=300)
     return chart


</code>
"
2175,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Manipur stations in 2017, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Manipur') & (data['Timestamp'].dt.year == 2017)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Manipur Stations 2017', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Manipur') & (data['Timestamp'].dt.year == 2017)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Manipur Stations 2017', width=450, height=350)


</code>
"
2176,specific_pattern,Show a cumulative area chart of PM2.5 readings for Firozabad across 2024.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Firozabad') & (data['Timestamp'].dt.year == 2024)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Firozabad 2024', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Firozabad') & (data['Timestamp'].dt.year == 2024)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Firozabad 2024', width=600, height=300)
     return chart


</code>
"
2177,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Himachal Pradesh, Karnataka, and Telangana in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Himachal Pradesh', 'Karnataka', 'Telangana']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Himachal Pradesh, Karnataka, UP – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Himachal Pradesh', 'Karnataka', 'Telangana']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Himachal Pradesh, Karnataka, UP – 2024', width=550, height=320)
     return chart


</code>
"
2178,temporal_aggregation,Plot the weekly average PM2.5 for Bidar in 2021 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Bidar') & (data['Timestamp'].dt.year == 2021)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Bidar 2021', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Bidar') & (data['Timestamp'].dt.year == 2021)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Bidar 2021', width=600, height=300)
     return chart


</code>
"
2179,temporal_aggregation,Show the monthly average PM2.5 for Amaravati in 2017 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Amaravati') & (data['Timestamp'].dt.year == 2017)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Amaravati 2017', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Amaravati') & (data['Timestamp'].dt.year == 2017)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Amaravati 2017', width=450, height=280)


</code>
"
2180,spatial_aggregation,"Show the top 6 states by average PM10 in 2020 as a bar chart, sorted in descending order.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2020]
    df = df.groupby('state')['PM10'].mean().reset_index().dropna()
    df = df.nlargest(6, 'PM10')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Top 6 States by Average PM10 in 2020', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2020]
     df = df.groupby('state')['PM10'].mean().reset_index().dropna()
     df = df.nlargest(6, 'PM10')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Top 6 States by Average PM10 in 2020', width=500, height=300)
     return chart


</code>
"
2181,spatial_aggregation,Show a bar chart of the top 12 cities by median PM2.5 in 2019.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2019]
    df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
    df = df.nlargest(12, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
        y=alt.Y('city:N', sort='-x', title='City'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
        tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 12 Cities by Median PM2.5 in 2019', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2019]
     df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
     df = df.nlargest(12, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
         y=alt.Y('city:N', sort='-x', title='City'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
         tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 12 Cities by Median PM2.5 in 2019', width=500, height=300)
     return chart


</code>
"
2182,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Odisha, Mizoram, and Nagaland across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Odisha', 'Mizoram', 'Nagaland']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Odisha', 'Mizoram', 'Nagaland']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
2183,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for West Bengal, Maharashtra, and Puducherry across 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['West Bengal', 'Maharashtra', 'Puducherry']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['West Bengal', 'Maharashtra', 'Puducherry']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
     return chart


</code>
"
2184,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Jalgaon, Bhilai, and Bhopal in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Jalgaon', 'Bhilai', 'Bhopal']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Jalgaon vs Bhilai vs Bhopal – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Jalgaon', 'Bhilai', 'Bhopal']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Jalgaon vs Bhilai vs Bhopal – 2022', width=550, height=320)
     return chart


</code>
"
2185,specific_pattern,Show a cumulative area chart of PM2.5 readings for Nashik across 2022.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Nashik') & (data['Timestamp'].dt.year == 2022)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Nashik 2022', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Nashik') & (data['Timestamp'].dt.year == 2022)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Nashik 2022', width=600, height=300)
     return chart


</code>
"
2186,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Sirsa, Thiruvananthapuram, and Byrnihat in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Sirsa', 'Thiruvananthapuram', 'Byrnihat']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Sirsa vs Thiruvananthapuram vs Byrnihat – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Sirsa', 'Thiruvananthapuram', 'Byrnihat']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Sirsa vs Thiruvananthapuram vs Byrnihat – 2022', width=550, height=320)
     return chart


</code>
"
2187,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Puducherry, West Bengal, and Chandigarh across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Puducherry', 'West Bengal', 'Chandigarh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Puducherry', 'West Bengal', 'Chandigarh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
2188,temporal_aggregation,Show the monthly average PM10 trend for Yadgir from 2017 to 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Yadgir'].copy()
    df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Yadgir (2017–2022)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Yadgir'].copy()
     df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Yadgir (2017–2022)', width=600, height=300)
     return chart


</code>
"
2189,specific_pattern,Show a cumulative area chart of PM2.5 readings for Vapi across 2019.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Vapi') & (data['Timestamp'].dt.year == 2019)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Vapi 2019', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Vapi') & (data['Timestamp'].dt.year == 2019)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Vapi 2019', width=600, height=300)
     return chart


</code>
"
2190,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Assam, Uttar Pradesh, and Haryana across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Assam', 'Uttar Pradesh', 'Haryana']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Assam', 'Uttar Pradesh', 'Haryana']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
2191,temporal_aggregation,Show the monthly average PM10 trend for Anantapur from 2017 to 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Anantapur'].copy()
    df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Anantapur (2017–2022)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Anantapur'].copy()
     df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Anantapur (2017–2022)', width=600, height=300)
     return chart


</code>
"
2192,spatial_aggregation,Plot the top 7 states by average PM2.5 in 2019 as a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2019]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nlargest(7, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 7 States by Average PM2.5 in 2019', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2019]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nlargest(7, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 7 States by Average PM2.5 in 2019', width=500, height=300)
     return chart


</code>
"
2193,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Tamil Nadu, Maharashtra, and Delhi in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Tamil Nadu', 'Maharashtra', 'Delhi']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Tamil Nadu, Maharashtra, UP – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Tamil Nadu', 'Maharashtra', 'Delhi']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Tamil Nadu, Maharashtra, UP – 2024', width=550, height=320)
     return chart


</code>
"
2194,temporal_aggregation,Show the monthly average PM2.5 for Sawai Madhopur in 2023 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Sawai Madhopur') & (data['Timestamp'].dt.year == 2023)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Sawai Madhopur 2023', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Sawai Madhopur') & (data['Timestamp'].dt.year == 2023)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Sawai Madhopur 2023', width=450, height=280)


</code>
"
2195,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Kerala, Sikkim, and Telangana in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Kerala', 'Sikkim', 'Telangana']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Kerala, Sikkim, UP – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Kerala', 'Sikkim', 'Telangana']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Kerala, Sikkim, UP – 2023', width=550, height=320)
     return chart


</code>
"
2196,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Telangana, Kerala, and Haryana from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Telangana', 'Kerala', 'Haryana']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Telangana vs Kerala vs Haryana', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Telangana', 'Kerala', 'Haryana']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Telangana vs Kerala vs Haryana', width=550, height=320)
     return chart


</code>
"
2197,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Tripura, Sikkim, and Jammu and Kashmir across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Tripura', 'Sikkim', 'Jammu and Kashmir']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Tripura', 'Sikkim', 'Jammu and Kashmir']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
2198,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Rajasthan, Chandigarh, and Uttarakhand from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Rajasthan', 'Chandigarh', 'Uttarakhand']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Rajasthan vs Chandigarh vs Uttarakhand', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Rajasthan', 'Chandigarh', 'Uttarakhand']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Rajasthan vs Chandigarh vs Uttarakhand', width=550, height=320)
     return chart


</code>
"
2199,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Himachal Pradesh, Manipur, and Maharashtra in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Himachal Pradesh', 'Manipur', 'Maharashtra']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Himachal Pradesh, Manipur, UP – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Himachal Pradesh', 'Manipur', 'Maharashtra']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Himachal Pradesh, Manipur, UP – 2022', width=550, height=320)
     return chart


</code>
"
2200,specific_pattern,Show a cumulative area chart of PM2.5 readings for Visakhapatnam across 2022.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Visakhapatnam') & (data['Timestamp'].dt.year == 2022)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Visakhapatnam 2022', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Visakhapatnam') & (data['Timestamp'].dt.year == 2022)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Visakhapatnam 2022', width=600, height=300)
     return chart


</code>
"
2201,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Katni, Kanchipuram, and Kalyan in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Katni', 'Kanchipuram', 'Kalyan']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Katni vs Kanchipuram vs Kalyan – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Katni', 'Kanchipuram', 'Kalyan']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Katni vs Kanchipuram vs Kalyan – 2023', width=550, height=320)
     return chart


</code>
"
2202,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Jammu and Kashmir, Uttar Pradesh, and Uttarakhand from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Jammu and Kashmir', 'Uttar Pradesh', 'Uttarakhand']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Jammu and Kashmir vs Uttar Pradesh vs Uttarakhand', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Jammu and Kashmir', 'Uttar Pradesh', 'Uttarakhand']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Jammu and Kashmir vs Uttar Pradesh vs Uttarakhand', width=550, height=320)
     return chart


</code>
"
2203,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Jharkhand, Delhi, and Himachal Pradesh in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Jharkhand', 'Delhi', 'Himachal Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Jharkhand, Delhi, UP – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Jharkhand', 'Delhi', 'Himachal Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Jharkhand, Delhi, UP – 2022', width=550, height=320)
     return chart


</code>
"
2204,temporal_aggregation,Show the monthly average PM2.5 for Maihar in 2024 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Maihar') & (data['Timestamp'].dt.year == 2024)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Maihar 2024', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Maihar') & (data['Timestamp'].dt.year == 2024)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Maihar 2024', width=450, height=280)


</code>
"
2205,temporal_aggregation,Show the monthly average PM2.5 for Chittoor in 2024 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Chittoor') & (data['Timestamp'].dt.year == 2024)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Chittoor 2024', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Chittoor') & (data['Timestamp'].dt.year == 2024)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Chittoor 2024', width=450, height=280)


</code>
"
2206,temporal_aggregation,Plot the weekly average PM2.5 for Sagar in 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Sagar') & (data['Timestamp'].dt.year == 2024)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Sagar 2024', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Sagar') & (data['Timestamp'].dt.year == 2024)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Sagar 2024', width=600, height=300)
     return chart


</code>
"
2207,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Tamil Nadu, Nagaland, and Odisha in 2018 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Tamil Nadu', 'Nagaland', 'Odisha']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2018)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Tamil Nadu, Nagaland, UP – 2018', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Tamil Nadu', 'Nagaland', 'Odisha']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2018)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Tamil Nadu, Nagaland, UP – 2018', width=550, height=320)
     return chart


</code>
"
2208,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Punjab, Maharashtra, and Haryana in 2021 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Punjab', 'Maharashtra', 'Haryana']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Punjab, Maharashtra, UP – 2021', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Punjab', 'Maharashtra', 'Haryana']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Punjab, Maharashtra, UP – 2021', width=550, height=320)
     return chart


</code>
"
2209,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Chandrapur, Jhansi, and Belapur in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Chandrapur', 'Jhansi', 'Belapur']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Chandrapur vs Jhansi vs Belapur – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Chandrapur', 'Jhansi', 'Belapur']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Chandrapur vs Jhansi vs Belapur – 2022', width=550, height=320)
     return chart


</code>
"
2210,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Haryana stations in 2018, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Haryana') & (data['Timestamp'].dt.year == 2018)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Haryana Stations 2018', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Haryana') & (data['Timestamp'].dt.year == 2018)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Haryana Stations 2018', width=450, height=350)


</code>
"
2211,specific_pattern,Plot the rolling 30-day average PM2.5 for Delhi in 2020 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Delhi') & (data['Timestamp'].dt.year == 2020)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Delhi 2020', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Delhi') & (data['Timestamp'].dt.year == 2020)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Delhi 2020', width=600, height=300)


</code>
"
2212,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Gujarat, Mizoram, and Nagaland across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Gujarat', 'Mizoram', 'Nagaland']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Gujarat', 'Mizoram', 'Nagaland']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
2213,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Meghalaya, Andhra Pradesh, and Mizoram in 2018 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Meghalaya', 'Andhra Pradesh', 'Mizoram']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2018)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Meghalaya, Andhra Pradesh, UP – 2018', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Meghalaya', 'Andhra Pradesh', 'Mizoram']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2018)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Meghalaya, Andhra Pradesh, UP – 2018', width=550, height=320)
     return chart


</code>
"
2214,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Haryana, Andhra Pradesh, and Bihar from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Haryana', 'Andhra Pradesh', 'Bihar']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Haryana vs Andhra Pradesh vs Bihar', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Haryana', 'Andhra Pradesh', 'Bihar']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Haryana vs Andhra Pradesh vs Bihar', width=550, height=320)
     return chart


</code>
"
2215,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Kerala, Himachal Pradesh, and Mizoram in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Kerala', 'Himachal Pradesh', 'Mizoram']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Kerala, Himachal Pradesh, UP – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Kerala', 'Himachal Pradesh', 'Mizoram']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Kerala, Himachal Pradesh, UP – 2023', width=550, height=320)
     return chart


</code>
"
2216,temporal_aggregation,Plot the weekly average PM2.5 for Sirsa in 2023 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Sirsa') & (data['Timestamp'].dt.year == 2023)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Sirsa 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Sirsa') & (data['Timestamp'].dt.year == 2023)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Sirsa 2023', width=600, height=300)
     return chart


</code>
"
2217,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Jammu and Kashmir, Maharashtra, and Andhra Pradesh across 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Jammu and Kashmir', 'Maharashtra', 'Andhra Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Jammu and Kashmir', 'Maharashtra', 'Andhra Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
     return chart


</code>
"
2218,specific_pattern,Show a cumulative area chart of PM2.5 readings for Naharlagun across 2023.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Naharlagun') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Naharlagun 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Naharlagun') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Naharlagun 2023', width=600, height=300)
     return chart


</code>
"
2219,temporal_aggregation,Show a monthly bar chart of the number of days Karnataka exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2024.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Karnataka') & (data['Timestamp'].dt.year == 2024)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Karnataka Exceeded WHO PM2.5 Guideline per Month – 2024', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Karnataka') & (data['Timestamp'].dt.year == 2024)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Karnataka Exceeded WHO PM2.5 Guideline per Month – 2024', width=500, height=300)
     return chart


</code>
"
2220,temporal_aggregation,Plot the weekly average PM2.5 for Kota in 2018 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Kota') & (data['Timestamp'].dt.year == 2018)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Kota 2018', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Kota') & (data['Timestamp'].dt.year == 2018)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Kota 2018', width=600, height=300)
     return chart


</code>
"
2221,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Gujarat, Himachal Pradesh, and Gujarat in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Gujarat', 'Himachal Pradesh', 'Gujarat']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Gujarat, Himachal Pradesh, UP – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Gujarat', 'Himachal Pradesh', 'Gujarat']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Gujarat, Himachal Pradesh, UP – 2023', width=550, height=320)
     return chart


</code>
"
2222,specific_pattern,Plot the rolling 30-day average PM2.5 for Gujarat in 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Gujarat') & (data['Timestamp'].dt.year == 2022)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Gujarat 2022', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Gujarat') & (data['Timestamp'].dt.year == 2022)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Gujarat 2022', width=600, height=300)


</code>
"
2223,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Muzaffarpur, Kalyan, and Hubballi in 2019 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Muzaffarpur', 'Kalyan', 'Hubballi']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Muzaffarpur vs Kalyan vs Hubballi – 2019', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Muzaffarpur', 'Kalyan', 'Hubballi']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Muzaffarpur vs Kalyan vs Hubballi – 2019', width=550, height=320)
     return chart


</code>
"
2224,temporal_aggregation,Show the monthly average PM2.5 for Asansol in 2024 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Asansol') & (data['Timestamp'].dt.year == 2024)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Asansol 2024', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Asansol') & (data['Timestamp'].dt.year == 2024)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Asansol 2024', width=450, height=280)


</code>
"
2225,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Assam, Uttarakhand, and Punjab from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Assam', 'Uttarakhand', 'Punjab']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Assam vs Uttarakhand vs Punjab', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Assam', 'Uttarakhand', 'Punjab']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Assam vs Uttarakhand vs Punjab', width=550, height=320)
     return chart


</code>
"
2226,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Tripura, Chhattisgarh, and Uttarakhand across 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Tripura', 'Chhattisgarh', 'Uttarakhand']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Tripura', 'Chhattisgarh', 'Uttarakhand']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
     return chart


</code>
"
2227,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Himachal Pradesh, Chhattisgarh, and Himachal Pradesh from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Himachal Pradesh', 'Chhattisgarh', 'Himachal Pradesh']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Himachal Pradesh vs Chhattisgarh vs Himachal Pradesh', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Himachal Pradesh', 'Chhattisgarh', 'Himachal Pradesh']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Himachal Pradesh vs Chhattisgarh vs Himachal Pradesh', width=550, height=320)
     return chart


</code>
"
2228,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Tripura, Nagaland, and Assam from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Tripura', 'Nagaland', 'Assam']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Tripura vs Nagaland vs Assam', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Tripura', 'Nagaland', 'Assam']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Tripura vs Nagaland vs Assam', width=550, height=320)
     return chart


</code>
"
2229,temporal_aggregation,Show a monthly bar chart of the number of days Andhra Pradesh exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2018.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Andhra Pradesh') & (data['Timestamp'].dt.year == 2018)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Andhra Pradesh Exceeded WHO PM2.5 Guideline per Month – 2018', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Andhra Pradesh') & (data['Timestamp'].dt.year == 2018)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Andhra Pradesh Exceeded WHO PM2.5 Guideline per Month – 2018', width=500, height=300)
     return chart


</code>
"
2230,temporal_aggregation,Show the monthly average PM2.5 for Bettiah in 2022 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Bettiah') & (data['Timestamp'].dt.year == 2022)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Bettiah 2022', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Bettiah') & (data['Timestamp'].dt.year == 2022)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Bettiah 2022', width=450, height=280)


</code>
"
2231,spatial_aggregation,Show a bar chart of the top 6 cities by median PM2.5 in 2024.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2024]
    df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
    df = df.nlargest(6, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
        y=alt.Y('city:N', sort='-x', title='City'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
        tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 6 Cities by Median PM2.5 in 2024', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2024]
     df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
     df = df.nlargest(6, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
         y=alt.Y('city:N', sort='-x', title='City'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
         tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 6 Cities by Median PM2.5 in 2024', width=500, height=300)
     return chart


</code>
"
2232,temporal_aggregation,Show the monthly average PM10 trend for Bahadurgarh from 2017 to 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Bahadurgarh'].copy()
    df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Bahadurgarh (2017–2022)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Bahadurgarh'].copy()
     df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Bahadurgarh (2017–2022)', width=600, height=300)
     return chart


</code>
"
2233,temporal_aggregation,Show the monthly average PM10 trend for Mysuru from 2017 to 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Mysuru'].copy()
    df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Mysuru (2017–2022)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Mysuru'].copy()
     df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Mysuru (2017–2022)', width=600, height=300)
     return chart


</code>
"
2234,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Punjab, Chhattisgarh, and Bihar in 2021 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Punjab', 'Chhattisgarh', 'Bihar']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Punjab, Chhattisgarh, UP – 2021', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Punjab', 'Chhattisgarh', 'Bihar']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Punjab, Chhattisgarh, UP – 2021', width=550, height=320)
     return chart


</code>
"
2235,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Nagaland, Odisha, and Sikkim in 2018 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Nagaland', 'Odisha', 'Sikkim']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2018)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Nagaland, Odisha, UP – 2018', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Nagaland', 'Odisha', 'Sikkim']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2018)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Nagaland, Odisha, UP – 2018', width=550, height=320)
     return chart


</code>
"
2236,temporal_aggregation,Show the monthly average PM10 trend for Katni from 2017 to 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Katni'].copy()
    df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Katni (2017–2022)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Katni'].copy()
     df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Katni (2017–2022)', width=600, height=300)
     return chart


</code>
"
2237,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Srinagar, Dharuhera, and Panipat in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Srinagar', 'Dharuhera', 'Panipat']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Srinagar vs Dharuhera vs Panipat – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Srinagar', 'Dharuhera', 'Panipat']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Srinagar vs Dharuhera vs Panipat – 2022', width=550, height=320)
     return chart


</code>
"
2238,temporal_aggregation,Show the monthly average PM2.5 for Amaravati in 2024 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Amaravati') & (data['Timestamp'].dt.year == 2024)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Amaravati 2024', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Amaravati') & (data['Timestamp'].dt.year == 2024)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Amaravati 2024', width=450, height=280)


</code>
"
2239,temporal_aggregation,Plot the weekly average PM2.5 for Chennai in 2020 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Chennai') & (data['Timestamp'].dt.year == 2020)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Chennai 2020', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Chennai') & (data['Timestamp'].dt.year == 2020)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Chennai 2020', width=600, height=300)
     return chart


</code>
"
2240,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Nagaland, Jharkhand, and Himachal Pradesh across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Nagaland', 'Jharkhand', 'Himachal Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Nagaland', 'Jharkhand', 'Himachal Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
2241,spatio_temporal_aggregation,"Create a faceted bar chart showing top 5 states by average PM2.5 per year for 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year.isin([2019,2020,2021,2022])].copy()
    df['Year'] = df['Timestamp'].dt.year
    agg = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    top = agg.groupby('Year').apply(lambda x: x.nlargest(5,'PM2.5')).reset_index(drop=True)
    chart = alt.Chart(top).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Avg PM2.5'),
        y=alt.Y('state:N', sort='-x'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).facet(facet='Year:O', columns=2).properties(title='Top 5 States by PM2.5 per Year')
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year.isin([2019,2020,2021,2022])].copy()
     df['Year'] = df['Timestamp'].dt.year
     agg = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     top = agg.groupby('Year').apply(lambda x: x.nlargest(5,'PM2.5')).reset_index(drop=True)
     chart = alt.Chart(top).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Avg PM2.5'),
         y=alt.Y('state:N', sort='-x'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).facet(facet='Year:O', columns=2).properties(title='Top 5 States by PM2.5 per Year')
     return chart


</code>
"
2242,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Puducherry, Meghalaya, and Sikkim across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Puducherry', 'Meghalaya', 'Sikkim']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Puducherry', 'Meghalaya', 'Sikkim']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
2243,temporal_aggregation,Show the monthly average PM2.5 for Nandesari in 2017 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Nandesari') & (data['Timestamp'].dt.year == 2017)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Nandesari 2017', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Nandesari') & (data['Timestamp'].dt.year == 2017)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Nandesari 2017', width=450, height=280)


</code>
"
2244,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Arunachal Pradesh, Andhra Pradesh, and Himachal Pradesh from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Arunachal Pradesh', 'Andhra Pradesh', 'Himachal Pradesh']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Arunachal Pradesh vs Andhra Pradesh vs Himachal Pradesh', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Arunachal Pradesh', 'Andhra Pradesh', 'Himachal Pradesh']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Arunachal Pradesh vs Andhra Pradesh vs Himachal Pradesh', width=550, height=320)
     return chart


</code>
"
2245,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Telangana stations in 2024, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Telangana') & (data['Timestamp'].dt.year == 2024)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Telangana Stations 2024', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Telangana') & (data['Timestamp'].dt.year == 2024)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Telangana Stations 2024', width=450, height=350)


</code>
"
2246,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Uttarakhand, Andhra Pradesh, and Delhi across 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Uttarakhand', 'Andhra Pradesh', 'Delhi']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Uttarakhand', 'Andhra Pradesh', 'Delhi']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
     return chart


</code>
"
2247,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Hassan, Barrackpore, and Silchar in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Hassan', 'Barrackpore', 'Silchar']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Hassan vs Barrackpore vs Silchar – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Hassan', 'Barrackpore', 'Silchar']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Hassan vs Barrackpore vs Silchar – 2023', width=550, height=320)
     return chart


</code>
"
2248,specific_pattern,Plot the rolling 30-day average PM2.5 for Bihar in 2020 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Bihar') & (data['Timestamp'].dt.year == 2020)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Bihar 2020', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Bihar') & (data['Timestamp'].dt.year == 2020)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Bihar 2020', width=600, height=300)


</code>
"
2249,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Madurai, Vatva, and Virudhunagar in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Madurai', 'Vatva', 'Virudhunagar']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Madurai vs Vatva vs Virudhunagar – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Madurai', 'Vatva', 'Virudhunagar']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Madurai vs Vatva vs Virudhunagar – 2022', width=550, height=320)
     return chart


</code>
"
2250,specific_pattern,Plot the rolling 30-day average PM2.5 for Tamil Nadu in 2019 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Tamil Nadu') & (data['Timestamp'].dt.year == 2019)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Tamil Nadu 2019', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Tamil Nadu') & (data['Timestamp'].dt.year == 2019)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Tamil Nadu 2019', width=600, height=300)


</code>
"
2251,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Andhra Pradesh, Telangana, and Kerala in 2018 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Andhra Pradesh', 'Telangana', 'Kerala']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2018)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Andhra Pradesh, Telangana, UP – 2018', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Andhra Pradesh', 'Telangana', 'Kerala']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2018)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Andhra Pradesh, Telangana, UP – 2018', width=550, height=320)
     return chart


</code>
"
2252,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Uttar Pradesh, Gujarat, and West Bengal from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Uttar Pradesh', 'Gujarat', 'West Bengal']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Uttar Pradesh vs Gujarat vs West Bengal', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Uttar Pradesh', 'Gujarat', 'West Bengal']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Uttar Pradesh vs Gujarat vs West Bengal', width=550, height=320)
     return chart


</code>
"
2253,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Karnataka stations in 2023, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Karnataka') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Karnataka Stations 2023', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Karnataka') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Karnataka Stations 2023', width=450, height=350)


</code>
"
2254,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Jammu and Kashmir stations in 2021, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Jammu and Kashmir') & (data['Timestamp'].dt.year == 2021)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Jammu and Kashmir Stations 2021', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Jammu and Kashmir') & (data['Timestamp'].dt.year == 2021)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Jammu and Kashmir Stations 2021', width=450, height=350)


</code>
"
2255,temporal_aggregation,Show a monthly bar chart of the number of days Maharashtra exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2022.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Maharashtra') & (data['Timestamp'].dt.year == 2022)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Maharashtra Exceeded WHO PM2.5 Guideline per Month – 2022', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Maharashtra') & (data['Timestamp'].dt.year == 2022)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Maharashtra Exceeded WHO PM2.5 Guideline per Month – 2022', width=500, height=300)
     return chart


</code>
"
2256,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Kerala, Andhra Pradesh, and Andhra Pradesh across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Kerala', 'Andhra Pradesh', 'Andhra Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Kerala', 'Andhra Pradesh', 'Andhra Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
2257,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Maharashtra, Uttarakhand, and Uttarakhand in 2021 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Maharashtra', 'Uttarakhand', 'Uttarakhand']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Maharashtra, Uttarakhand, UP – 2021', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Maharashtra', 'Uttarakhand', 'Uttarakhand']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Maharashtra, Uttarakhand, UP – 2021', width=550, height=320)
     return chart


</code>
"
2258,temporal_aggregation,Plot the weekly average PM2.5 for Pune in 2020 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Pune') & (data['Timestamp'].dt.year == 2020)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Pune 2020', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Pune') & (data['Timestamp'].dt.year == 2020)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Pune 2020', width=600, height=300)
     return chart


</code>
"
2259,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Nagaland, Gujarat, and Tamil Nadu in 2019 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Nagaland', 'Gujarat', 'Tamil Nadu']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Nagaland, Gujarat, UP – 2019', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Nagaland', 'Gujarat', 'Tamil Nadu']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Nagaland, Gujarat, UP – 2019', width=550, height=320)
     return chart


</code>
"
2260,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Uttar Pradesh, Uttarakhand, and Assam across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Uttar Pradesh', 'Uttarakhand', 'Assam']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Uttar Pradesh', 'Uttarakhand', 'Assam']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
2261,specific_pattern,Show a cumulative area chart of PM2.5 readings for Damoh across 2023.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Damoh') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Damoh 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Damoh') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Damoh 2023', width=600, height=300)
     return chart


</code>
"
2262,specific_pattern,Show a cumulative area chart of PM2.5 readings for Bhiwadi across 2019.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Bhiwadi') & (data['Timestamp'].dt.year == 2019)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Bhiwadi 2019', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Bhiwadi') & (data['Timestamp'].dt.year == 2019)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Bhiwadi 2019', width=600, height=300)
     return chart


</code>
"
2263,spatial_aggregation,Visualize the bottom 5 states with the lowest average PM2.5 in 2022 using a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2022]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nsmallest(5, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Bottom 5 States by Average PM2.5 in 2022', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2022]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nsmallest(5, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Bottom 5 States by Average PM2.5 in 2022', width=500, height=300)
     return chart


</code>
"
2264,temporal_aggregation,Show the monthly average PM2.5 for Dholpur in 2024 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Dholpur') & (data['Timestamp'].dt.year == 2024)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Dholpur 2024', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Dholpur') & (data['Timestamp'].dt.year == 2024)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Dholpur 2024', width=450, height=280)


</code>
"
2265,temporal_aggregation,Show the monthly average PM10 trend for Sagar from 2017 to 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Sagar'].copy()
    df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Sagar (2017–2022)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Sagar'].copy()
     df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Sagar (2017–2022)', width=600, height=300)
     return chart


</code>
"
2266,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Solapur, Bidar, and Thanjavur in 2018 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Solapur', 'Bidar', 'Thanjavur']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2018)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Solapur vs Bidar vs Thanjavur – 2018', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Solapur', 'Bidar', 'Thanjavur']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2018)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Solapur vs Bidar vs Thanjavur – 2018', width=550, height=320)
     return chart


</code>
"
2267,temporal_aggregation,Show a monthly bar chart of the number of days Maharashtra exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2023.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Maharashtra') & (data['Timestamp'].dt.year == 2023)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Maharashtra Exceeded WHO PM2.5 Guideline per Month – 2023', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Maharashtra') & (data['Timestamp'].dt.year == 2023)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Maharashtra Exceeded WHO PM2.5 Guideline per Month – 2023', width=500, height=300)
     return chart


</code>
"
2268,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Himachal Pradesh, Tripura, and Puducherry in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Himachal Pradesh', 'Tripura', 'Puducherry']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Himachal Pradesh, Tripura, UP – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Himachal Pradesh', 'Tripura', 'Puducherry']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Himachal Pradesh, Tripura, UP – 2024', width=550, height=320)
     return chart


</code>
"
2269,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Punjab, Chandigarh, and Arunachal Pradesh in 2021 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Punjab', 'Chandigarh', 'Arunachal Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Punjab, Chandigarh, UP – 2021', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Punjab', 'Chandigarh', 'Arunachal Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Punjab, Chandigarh, UP – 2021', width=550, height=320)
     return chart


</code>
"
2270,temporal_aggregation,Show the monthly average PM10 trend for Belgaum from 2017 to 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Belgaum'].copy()
    df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Belgaum (2017–2022)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Belgaum'].copy()
     df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Belgaum (2017–2022)', width=600, height=300)
     return chart


</code>
"
2271,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Kerala, Maharashtra, and Bihar in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Kerala', 'Maharashtra', 'Bihar']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Kerala, Maharashtra, UP – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Kerala', 'Maharashtra', 'Bihar']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Kerala, Maharashtra, UP – 2023', width=550, height=320)
     return chart


</code>
"
2272,temporal_aggregation,Show the monthly average PM2.5 for Barrackpore in 2019 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Barrackpore') & (data['Timestamp'].dt.year == 2019)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Barrackpore 2019', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Barrackpore') & (data['Timestamp'].dt.year == 2019)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Barrackpore 2019', width=450, height=280)


</code>
"
2273,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Saharsa, Solapur, and Fatehabad in 2020 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Saharsa', 'Solapur', 'Fatehabad']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2020)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Saharsa vs Solapur vs Fatehabad – 2020', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Saharsa', 'Solapur', 'Fatehabad']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2020)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Saharsa vs Solapur vs Fatehabad – 2020', width=550, height=320)
     return chart


</code>
"
2274,temporal_aggregation,Show the monthly average PM10 trend for Pratapgarh from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Pratapgarh'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Pratapgarh (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Pratapgarh'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Pratapgarh (2019–2024)', width=600, height=300)
     return chart


</code>
"
2275,temporal_aggregation,Show the monthly average PM2.5 for Eloor in 2023 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Eloor') & (data['Timestamp'].dt.year == 2023)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Eloor 2023', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Eloor') & (data['Timestamp'].dt.year == 2023)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Eloor 2023', width=450, height=280)


</code>
"
2276,temporal_aggregation,Show the monthly average PM2.5 for Baddi in 2020 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Baddi') & (data['Timestamp'].dt.year == 2020)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Baddi 2020', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Baddi') & (data['Timestamp'].dt.year == 2020)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Baddi 2020', width=450, height=280)


</code>
"
2277,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Kerala, Andhra Pradesh, and Arunachal Pradesh in 2021 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Kerala', 'Andhra Pradesh', 'Arunachal Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Kerala, Andhra Pradesh, UP – 2021', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Kerala', 'Andhra Pradesh', 'Arunachal Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Kerala, Andhra Pradesh, UP – 2021', width=550, height=320)
     return chart


</code>
"
2278,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Rajasthan, Meghalaya, and Jammu and Kashmir in 2021 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Rajasthan', 'Meghalaya', 'Jammu and Kashmir']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Rajasthan, Meghalaya, UP – 2021', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Rajasthan', 'Meghalaya', 'Jammu and Kashmir']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Rajasthan, Meghalaya, UP – 2021', width=550, height=320)
     return chart


</code>
"
2279,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Bihar, Andhra Pradesh, and Chandigarh in 2021 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Bihar', 'Andhra Pradesh', 'Chandigarh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Bihar, Andhra Pradesh, UP – 2021', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Bihar', 'Andhra Pradesh', 'Chandigarh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Bihar, Andhra Pradesh, UP – 2021', width=550, height=320)
     return chart


</code>
"
2280,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Patna, Jind, and Khanna in 2018 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Patna', 'Jind', 'Khanna']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2018)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Patna vs Jind vs Khanna – 2018', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Patna', 'Jind', 'Khanna']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2018)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Patna vs Jind vs Khanna – 2018', width=550, height=320)
     return chart


</code>
"
2281,temporal_aggregation,Show the monthly average PM2.5 for Jhalawar in 2022 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Jhalawar') & (data['Timestamp'].dt.year == 2022)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Jhalawar 2022', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Jhalawar') & (data['Timestamp'].dt.year == 2022)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Jhalawar 2022', width=450, height=280)


</code>
"
2282,specific_pattern,Show a cumulative area chart of PM2.5 readings for Gangtok across 2022.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Gangtok') & (data['Timestamp'].dt.year == 2022)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Gangtok 2022', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Gangtok') & (data['Timestamp'].dt.year == 2022)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Gangtok 2022', width=600, height=300)
     return chart


</code>
"
2283,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Tamil Nadu, Sikkim, and Manipur across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Tamil Nadu', 'Sikkim', 'Manipur']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Tamil Nadu', 'Sikkim', 'Manipur']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
2284,spatio_temporal_aggregation,Show a heatmap of average PM2.5 for the top 5 most polluted states by month for 2018.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    top8 = data.groupby('state')['PM2.5'].mean().nlargest(5).index.tolist()
    df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2018)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_rect().encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('state:N', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
        tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Heatmap – Top 5 Polluted States by Month (2018)', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     top8 = data.groupby('state')['PM2.5'].mean().nlargest(5).index.tolist()
     df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2018)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_rect().encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('state:N', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
         tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Heatmap – Top 5 Polluted States by Month (2018)', width=500, height=300)
     return chart


</code>
"
2285,temporal_aggregation,Show the monthly average PM10 trend for Siwan from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Siwan'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Siwan (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Siwan'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Siwan (2019–2024)', width=600, height=300)
     return chart


</code>
"
2286,temporal_aggregation,Show the monthly average PM10 trend for Nalbari from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Nalbari'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Nalbari (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Nalbari'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Nalbari (2019–2024)', width=600, height=300)
     return chart


</code>
"
2287,temporal_aggregation,Show the monthly average PM10 trend for Munger from 2017 to 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Munger'].copy()
    df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Munger (2017–2022)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Munger'].copy()
     df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Munger (2017–2022)', width=600, height=300)
     return chart


</code>
"
2288,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Chandrapur, Kunjemura, and Mahad in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Chandrapur', 'Kunjemura', 'Mahad']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Chandrapur vs Kunjemura vs Mahad – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Chandrapur', 'Kunjemura', 'Mahad']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Chandrapur vs Kunjemura vs Mahad – 2024', width=550, height=320)
     return chart


</code>
"
2289,specific_pattern,Show a cumulative area chart of PM2.5 readings for Kochi across 2022.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Kochi') & (data['Timestamp'].dt.year == 2022)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Kochi 2022', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Kochi') & (data['Timestamp'].dt.year == 2022)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Kochi 2022', width=600, height=300)
     return chart


</code>
"
2290,spatial_aggregation,Plot the top 12 states by average PM2.5 in 2017 as a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2017]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nlargest(12, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 12 States by Average PM2.5 in 2017', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2017]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nlargest(12, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 12 States by Average PM2.5 in 2017', width=500, height=300)
     return chart


</code>
"
2291,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Chhattisgarh, Jammu and Kashmir, and Odisha in 2017 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Chhattisgarh', 'Jammu and Kashmir', 'Odisha']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2017)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Chhattisgarh, Jammu and Kashmir, UP – 2017', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Chhattisgarh', 'Jammu and Kashmir', 'Odisha']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2017)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Chhattisgarh, Jammu and Kashmir, UP – 2017', width=550, height=320)
     return chart


</code>
"
2292,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Puducherry, Andhra Pradesh, and Manipur in 2021 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Puducherry', 'Andhra Pradesh', 'Manipur']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Puducherry, Andhra Pradesh, UP – 2021', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Puducherry', 'Andhra Pradesh', 'Manipur']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Puducherry, Andhra Pradesh, UP – 2021', width=550, height=320)
     return chart


</code>
"
2293,temporal_aggregation,Show the monthly average PM2.5 for Vatva in 2020 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Vatva') & (data['Timestamp'].dt.year == 2020)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Vatva 2020', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Vatva') & (data['Timestamp'].dt.year == 2020)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Vatva 2020', width=450, height=280)


</code>
"
2294,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Delhi, Kerala, and Nagaland in 2017 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Delhi', 'Kerala', 'Nagaland']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2017)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Delhi, Kerala, UP – 2017', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Delhi', 'Kerala', 'Nagaland']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2017)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Delhi, Kerala, UP – 2017', width=550, height=320)
     return chart


</code>
"
2295,temporal_aggregation,Show the monthly average PM2.5 for Tirupur in 2024 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Tirupur') & (data['Timestamp'].dt.year == 2024)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Tirupur 2024', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Tirupur') & (data['Timestamp'].dt.year == 2024)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Tirupur 2024', width=450, height=280)


</code>
"
2296,temporal_aggregation,Show the monthly average PM10 trend for Pathardih from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Pathardih'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Pathardih (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Pathardih'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Pathardih (2019–2024)', width=600, height=300)
     return chart


</code>
"
2297,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Chamarajanagar, Faridabad, and Chengalpattu in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Chamarajanagar', 'Faridabad', 'Chengalpattu']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Chamarajanagar vs Faridabad vs Chengalpattu – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Chamarajanagar', 'Faridabad', 'Chengalpattu']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Chamarajanagar vs Faridabad vs Chengalpattu – 2023', width=550, height=320)
     return chart


</code>
"
2298,temporal_aggregation,Show the monthly average PM10 trend for Hyderabad from 2017 to 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Hyderabad'].copy()
    df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Hyderabad (2017–2022)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Hyderabad'].copy()
     df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Hyderabad (2017–2022)', width=600, height=300)
     return chart


</code>
"
2299,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Rajasthan, Arunachal Pradesh, and Assam in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Rajasthan', 'Arunachal Pradesh', 'Assam']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Rajasthan, Arunachal Pradesh, UP – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Rajasthan', 'Arunachal Pradesh', 'Assam']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Rajasthan, Arunachal Pradesh, UP – 2023', width=550, height=320)
     return chart


</code>
"
2300,temporal_aggregation,Plot the weekly average PM2.5 for Bulandshahr in 2023 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Bulandshahr') & (data['Timestamp'].dt.year == 2023)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Bulandshahr 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Bulandshahr') & (data['Timestamp'].dt.year == 2023)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Bulandshahr 2023', width=600, height=300)
     return chart


</code>
"
2301,temporal_aggregation,Show the monthly average PM2.5 for Jalandhar in 2024 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Jalandhar') & (data['Timestamp'].dt.year == 2024)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Jalandhar 2024', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Jalandhar') & (data['Timestamp'].dt.year == 2024)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Jalandhar 2024', width=450, height=280)


</code>
"
2302,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Uttar Pradesh stations in 2017, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Uttar Pradesh') & (data['Timestamp'].dt.year == 2017)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Uttar Pradesh Stations 2017', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Uttar Pradesh') & (data['Timestamp'].dt.year == 2017)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Uttar Pradesh Stations 2017', width=450, height=350)


</code>
"
2303,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Sikkim, Sikkim, and Tamil Nadu across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Sikkim', 'Sikkim', 'Tamil Nadu']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Sikkim', 'Sikkim', 'Tamil Nadu']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
2304,temporal_aggregation,Show the monthly average PM2.5 for Karauli in 2023 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Karauli') & (data['Timestamp'].dt.year == 2023)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Karauli 2023', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Karauli') & (data['Timestamp'].dt.year == 2023)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Karauli 2023', width=450, height=280)


</code>
"
2305,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for West Bengal, Manipur, and Meghalaya from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['West Bengal', 'Manipur', 'Meghalaya']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: West Bengal vs Manipur vs Meghalaya', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['West Bengal', 'Manipur', 'Meghalaya']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: West Bengal vs Manipur vs Meghalaya', width=550, height=320)
     return chart


</code>
"
2306,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Tamil Nadu, Haryana, and Gujarat in 2019 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Tamil Nadu', 'Haryana', 'Gujarat']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Tamil Nadu, Haryana, UP – 2019', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Tamil Nadu', 'Haryana', 'Gujarat']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Tamil Nadu, Haryana, UP – 2019', width=550, height=320)
     return chart


</code>
"
2307,specific_pattern,Show a cumulative area chart of PM2.5 readings for Kannur across 2024.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Kannur') & (data['Timestamp'].dt.year == 2024)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Kannur 2024', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Kannur') & (data['Timestamp'].dt.year == 2024)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Kannur 2024', width=600, height=300)
     return chart


</code>
"
2308,temporal_aggregation,Show the monthly average PM2.5 for Lucknow in 2018 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Lucknow') & (data['Timestamp'].dt.year == 2018)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Lucknow 2018', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Lucknow') & (data['Timestamp'].dt.year == 2018)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Lucknow 2018', width=450, height=280)


</code>
"
2309,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for West Bengal, Meghalaya, and West Bengal from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['West Bengal', 'Meghalaya', 'West Bengal']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: West Bengal vs Meghalaya vs West Bengal', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['West Bengal', 'Meghalaya', 'West Bengal']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: West Bengal vs Meghalaya vs West Bengal', width=550, height=320)
     return chart


</code>
"
2310,spatial_aggregation,"Show the top 6 states by average PM10 in 2023 as a bar chart, sorted in descending order.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2023]
    df = df.groupby('state')['PM10'].mean().reset_index().dropna()
    df = df.nlargest(6, 'PM10')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Top 6 States by Average PM10 in 2023', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2023]
     df = df.groupby('state')['PM10'].mean().reset_index().dropna()
     df = df.nlargest(6, 'PM10')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Top 6 States by Average PM10 in 2023', width=500, height=300)
     return chart


</code>
"
2311,temporal_aggregation,Show the monthly average PM10 trend for Haldia from 2017 to 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Haldia'].copy()
    df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Haldia (2017–2022)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Haldia'].copy()
     df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Haldia (2017–2022)', width=600, height=300)
     return chart


</code>
"
2312,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Assam, Bihar, and Tamil Nadu in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Assam', 'Bihar', 'Tamil Nadu']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Assam, Bihar, UP – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Assam', 'Bihar', 'Tamil Nadu']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Assam, Bihar, UP – 2022', width=550, height=320)
     return chart


</code>
"
2313,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Bagalkot, Jaisalmer, and Udaipur in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Bagalkot', 'Jaisalmer', 'Udaipur']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Bagalkot vs Jaisalmer vs Udaipur – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Bagalkot', 'Jaisalmer', 'Udaipur']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Bagalkot vs Jaisalmer vs Udaipur – 2024', width=550, height=320)
     return chart


</code>
"
2314,specific_pattern,Show a cumulative area chart of PM2.5 readings for Ahmednagar across 2023.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Ahmednagar') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Ahmednagar 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Ahmednagar') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Ahmednagar 2023', width=600, height=300)
     return chart


</code>
"
2315,temporal_aggregation,Plot the weekly average PM2.5 for Pathardih in 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Pathardih') & (data['Timestamp'].dt.year == 2024)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Pathardih 2024', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Pathardih') & (data['Timestamp'].dt.year == 2024)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Pathardih 2024', width=600, height=300)
     return chart


</code>
"
2316,spatial_aggregation,Plot the top 10 states by average PM2.5 in 2021 as a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2021]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nlargest(10, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 10 States by Average PM2.5 in 2021', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2021]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nlargest(10, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 10 States by Average PM2.5 in 2021', width=500, height=300)
     return chart


</code>
"
2317,temporal_aggregation,Show the monthly average PM2.5 for Davanagere in 2018 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Davanagere') & (data['Timestamp'].dt.year == 2018)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Davanagere 2018', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Davanagere') & (data['Timestamp'].dt.year == 2018)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Davanagere 2018', width=450, height=280)


</code>
"
2318,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Rajasthan, Chhattisgarh, and Arunachal Pradesh from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Rajasthan', 'Chhattisgarh', 'Arunachal Pradesh']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Rajasthan vs Chhattisgarh vs Arunachal Pradesh', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Rajasthan', 'Chhattisgarh', 'Arunachal Pradesh']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Rajasthan vs Chhattisgarh vs Arunachal Pradesh', width=550, height=320)
     return chart


</code>
"
2319,temporal_aggregation,Show the monthly average PM2.5 for Kishanganj in 2018 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Kishanganj') & (data['Timestamp'].dt.year == 2018)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Kishanganj 2018', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Kishanganj') & (data['Timestamp'].dt.year == 2018)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Kishanganj 2018', width=450, height=280)


</code>
"
2320,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Nagaland, Arunachal Pradesh, and Chandigarh from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Nagaland', 'Arunachal Pradesh', 'Chandigarh']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Nagaland vs Arunachal Pradesh vs Chandigarh', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Nagaland', 'Arunachal Pradesh', 'Chandigarh']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Nagaland vs Arunachal Pradesh vs Chandigarh', width=550, height=320)
     return chart


</code>
"
2321,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Assam stations in 2017, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Assam') & (data['Timestamp'].dt.year == 2017)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Assam Stations 2017', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Assam') & (data['Timestamp'].dt.year == 2017)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Assam Stations 2017', width=450, height=350)


</code>
"
2322,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Punjab, Chhattisgarh, and Telangana from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Punjab', 'Chhattisgarh', 'Telangana']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Punjab vs Chhattisgarh vs Telangana', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Punjab', 'Chhattisgarh', 'Telangana']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Punjab vs Chhattisgarh vs Telangana', width=550, height=320)
     return chart


</code>
"
2323,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Rajasthan, Meghalaya, and Punjab from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Rajasthan', 'Meghalaya', 'Punjab']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Rajasthan vs Meghalaya vs Punjab', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Rajasthan', 'Meghalaya', 'Punjab']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Rajasthan vs Meghalaya vs Punjab', width=550, height=320)
     return chart


</code>
"
2324,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Durgapur, Chikkaballapur, and Vellore in 2019 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Durgapur', 'Chikkaballapur', 'Vellore']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Durgapur vs Chikkaballapur vs Vellore – 2019', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Durgapur', 'Chikkaballapur', 'Vellore']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Durgapur vs Chikkaballapur vs Vellore – 2019', width=550, height=320)
     return chart


</code>
"
2325,specific_pattern,Show a cumulative area chart of PM2.5 readings for Bhiwandi across 2023.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Bhiwandi') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Bhiwandi 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Bhiwandi') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Bhiwandi 2023', width=600, height=300)
     return chart


</code>
"
2326,specific_pattern,Plot the rolling 30-day average PM2.5 for Tamil Nadu in 2017 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Tamil Nadu') & (data['Timestamp'].dt.year == 2017)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Tamil Nadu 2017', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Tamil Nadu') & (data['Timestamp'].dt.year == 2017)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Tamil Nadu 2017', width=600, height=300)


</code>
"
2327,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Odisha, Jharkhand, and Jammu and Kashmir in 2021 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Odisha', 'Jharkhand', 'Jammu and Kashmir']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Odisha, Jharkhand, UP – 2021', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Odisha', 'Jharkhand', 'Jammu and Kashmir']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Odisha, Jharkhand, UP – 2021', width=550, height=320)
     return chart


</code>
"
2328,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Tamil Nadu, Chandigarh, and Haryana in 2021 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Tamil Nadu', 'Chandigarh', 'Haryana']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Tamil Nadu, Chandigarh, UP – 2021', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Tamil Nadu', 'Chandigarh', 'Haryana']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Tamil Nadu, Chandigarh, UP – 2021', width=550, height=320)
     return chart


</code>
"
2329,temporal_aggregation,Show the monthly average PM10 trend for Baripada from 2017 to 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Baripada'].copy()
    df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Baripada (2017–2022)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Baripada'].copy()
     df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Baripada (2017–2022)', width=600, height=300)
     return chart


</code>
"
2330,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Satna, Vatva, and Dungarpur in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Satna', 'Vatva', 'Dungarpur']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Satna vs Vatva vs Dungarpur – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Satna', 'Vatva', 'Dungarpur']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Satna vs Vatva vs Dungarpur – 2024', width=550, height=320)
     return chart


</code>
"
2331,temporal_aggregation,Show the monthly average PM2.5 for Barmer in 2022 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Barmer') & (data['Timestamp'].dt.year == 2022)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Barmer 2022', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Barmer') & (data['Timestamp'].dt.year == 2022)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Barmer 2022', width=450, height=280)


</code>
"
2332,specific_pattern,Show a cumulative area chart of PM2.5 readings for Kishanganj across 2021.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Kishanganj') & (data['Timestamp'].dt.year == 2021)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Kishanganj 2021', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Kishanganj') & (data['Timestamp'].dt.year == 2021)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Kishanganj 2021', width=600, height=300)
     return chart


</code>
"
2333,temporal_aggregation,Show a monthly bar chart of the number of days Mizoram exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2022.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Mizoram') & (data['Timestamp'].dt.year == 2022)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Mizoram Exceeded WHO PM2.5 Guideline per Month – 2022', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Mizoram') & (data['Timestamp'].dt.year == 2022)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Mizoram Exceeded WHO PM2.5 Guideline per Month – 2022', width=500, height=300)
     return chart


</code>
"
2334,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Punjab stations in 2024, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Punjab') & (data['Timestamp'].dt.year == 2024)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Punjab Stations 2024', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Punjab') & (data['Timestamp'].dt.year == 2024)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Punjab Stations 2024', width=450, height=350)


</code>
"
2335,temporal_aggregation,Show the monthly average PM10 trend for Tirupur from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Tirupur'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Tirupur (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Tirupur'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Tirupur (2019–2024)', width=600, height=300)
     return chart


</code>
"
2336,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Bihar, West Bengal, and Himachal Pradesh across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Bihar', 'West Bengal', 'Himachal Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Bihar', 'West Bengal', 'Himachal Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
2337,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Navi Mumbai, Baddi, and Sagar in 2017 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Navi Mumbai', 'Baddi', 'Sagar']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2017)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Navi Mumbai vs Baddi vs Sagar – 2017', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Navi Mumbai', 'Baddi', 'Sagar']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2017)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Navi Mumbai vs Baddi vs Sagar – 2017', width=550, height=320)
     return chart


</code>
"
2338,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Rajasthan, Jharkhand, and Puducherry from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Rajasthan', 'Jharkhand', 'Puducherry']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Rajasthan vs Jharkhand vs Puducherry', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Rajasthan', 'Jharkhand', 'Puducherry']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Rajasthan vs Jharkhand vs Puducherry', width=550, height=320)
     return chart


</code>
"
2339,specific_pattern,Plot the rolling 30-day average PM2.5 for Himachal Pradesh in 2018 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Himachal Pradesh') & (data['Timestamp'].dt.year == 2018)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Himachal Pradesh 2018', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Himachal Pradesh') & (data['Timestamp'].dt.year == 2018)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Himachal Pradesh 2018', width=600, height=300)


</code>
"
2340,temporal_aggregation,Show the monthly average PM2.5 for Amritsar in 2024 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Amritsar') & (data['Timestamp'].dt.year == 2024)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Amritsar 2024', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Amritsar') & (data['Timestamp'].dt.year == 2024)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Amritsar 2024', width=450, height=280)


</code>
"
2341,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Manipur, Manipur, and Chhattisgarh in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Manipur', 'Manipur', 'Chhattisgarh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Manipur, Manipur, UP – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Manipur', 'Manipur', 'Chhattisgarh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Manipur, Manipur, UP – 2022', width=550, height=320)
     return chart


</code>
"
2342,temporal_aggregation,Show the monthly average PM2.5 for Tirupati in 2024 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Tirupati') & (data['Timestamp'].dt.year == 2024)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Tirupati 2024', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Tirupati') & (data['Timestamp'].dt.year == 2024)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Tirupati 2024', width=450, height=280)


</code>
"
2343,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Ooty, Chandigarh, and Kozhikode in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Ooty', 'Chandigarh', 'Kozhikode']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Ooty vs Chandigarh vs Kozhikode – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Ooty', 'Chandigarh', 'Kozhikode']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Ooty vs Chandigarh vs Kozhikode – 2022', width=550, height=320)
     return chart


</code>
"
2344,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Tamil Nadu, Bihar, and Chandigarh from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Tamil Nadu', 'Bihar', 'Chandigarh']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Tamil Nadu vs Bihar vs Chandigarh', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Tamil Nadu', 'Bihar', 'Chandigarh']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Tamil Nadu vs Bihar vs Chandigarh', width=550, height=320)
     return chart


</code>
"
2345,temporal_aggregation,Show the monthly average PM2.5 for Hanumangarh in 2020 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Hanumangarh') & (data['Timestamp'].dt.year == 2020)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Hanumangarh 2020', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Hanumangarh') & (data['Timestamp'].dt.year == 2020)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Hanumangarh 2020', width=450, height=280)


</code>
"
2346,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Arunachal Pradesh, Kerala, and Uttar Pradesh from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Arunachal Pradesh', 'Kerala', 'Uttar Pradesh']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Arunachal Pradesh vs Kerala vs Uttar Pradesh', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Arunachal Pradesh', 'Kerala', 'Uttar Pradesh']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Arunachal Pradesh vs Kerala vs Uttar Pradesh', width=550, height=320)
     return chart


</code>
"
2347,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Meghalaya stations in 2024, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Meghalaya') & (data['Timestamp'].dt.year == 2024)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Meghalaya Stations 2024', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Meghalaya') & (data['Timestamp'].dt.year == 2024)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Meghalaya Stations 2024', width=450, height=350)


</code>
"
2348,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Patna, Chamarajanagar, and Kashipur in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Patna', 'Chamarajanagar', 'Kashipur']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Patna vs Chamarajanagar vs Kashipur – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Patna', 'Chamarajanagar', 'Kashipur']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Patna vs Chamarajanagar vs Kashipur – 2023', width=550, height=320)
     return chart


</code>
"
2349,temporal_aggregation,Plot the weekly average PM2.5 for Mysuru in 2020 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Mysuru') & (data['Timestamp'].dt.year == 2020)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Mysuru 2020', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Mysuru') & (data['Timestamp'].dt.year == 2020)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Mysuru 2020', width=600, height=300)
     return chart


</code>
"
2350,temporal_aggregation,Show the monthly average PM10 trend for Bettiah from 2017 to 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Bettiah'].copy()
    df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Bettiah (2017–2022)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Bettiah'].copy()
     df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Bettiah (2017–2022)', width=600, height=300)
     return chart


</code>
"
2351,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Karnataka, Maharashtra, and Manipur from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Karnataka', 'Maharashtra', 'Manipur']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Karnataka vs Maharashtra vs Manipur', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Karnataka', 'Maharashtra', 'Manipur']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Karnataka vs Maharashtra vs Manipur', width=550, height=320)
     return chart


</code>
"
2352,spatial_aggregation,Show a bar chart of the top 9 cities by median PM2.5 in 2017.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2017]
    df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
    df = df.nlargest(9, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
        y=alt.Y('city:N', sort='-x', title='City'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
        tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 9 Cities by Median PM2.5 in 2017', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2017]
     df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
     df = df.nlargest(9, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
         y=alt.Y('city:N', sort='-x', title='City'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
         tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 9 Cities by Median PM2.5 in 2017', width=500, height=300)
     return chart


</code>
"
2353,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for West Bengal, Rajasthan, and Tamil Nadu across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['West Bengal', 'Rajasthan', 'Tamil Nadu']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['West Bengal', 'Rajasthan', 'Tamil Nadu']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
2354,temporal_aggregation,Show the monthly average PM10 trend for Ambala from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Ambala'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Ambala (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Ambala'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Ambala (2019–2024)', width=600, height=300)
     return chart


</code>
"
2355,temporal_aggregation,Plot the weekly average PM2.5 for Mandideep in 2019 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Mandideep') & (data['Timestamp'].dt.year == 2019)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Mandideep 2019', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Mandideep') & (data['Timestamp'].dt.year == 2019)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Mandideep 2019', width=600, height=300)
     return chart


</code>
"
2356,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Kerala, Bihar, and Mizoram across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Kerala', 'Bihar', 'Mizoram']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Kerala', 'Bihar', 'Mizoram']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
2357,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Puducherry, Tripura, and Uttarakhand in 2021 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Puducherry', 'Tripura', 'Uttarakhand']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Puducherry, Tripura, UP – 2021', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Puducherry', 'Tripura', 'Uttarakhand']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Puducherry, Tripura, UP – 2021', width=550, height=320)
     return chart


</code>
"
2358,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Delhi stations in 2017, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Delhi') & (data['Timestamp'].dt.year == 2017)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Delhi Stations 2017', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Delhi') & (data['Timestamp'].dt.year == 2017)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Delhi Stations 2017', width=450, height=350)


</code>
"
2359,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Odisha, Nagaland, and Jharkhand from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Odisha', 'Nagaland', 'Jharkhand']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Odisha vs Nagaland vs Jharkhand', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Odisha', 'Nagaland', 'Jharkhand']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Odisha vs Nagaland vs Jharkhand', width=550, height=320)
     return chart


</code>
"
2360,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Pithampur, Palwal , and Dhanbad in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Pithampur', 'Palwal ', 'Dhanbad']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Pithampur vs Palwal  vs Dhanbad – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Pithampur', 'Palwal ', 'Dhanbad']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Pithampur vs Palwal  vs Dhanbad – 2022', width=550, height=320)
     return chart


</code>
"
2361,spatial_aggregation,"Show the top 13 states by average PM10 in 2024 as a bar chart, sorted in descending order.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2024]
    df = df.groupby('state')['PM10'].mean().reset_index().dropna()
    df = df.nlargest(13, 'PM10')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Top 13 States by Average PM10 in 2024', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2024]
     df = df.groupby('state')['PM10'].mean().reset_index().dropna()
     df = df.nlargest(13, 'PM10')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Top 13 States by Average PM10 in 2024', width=500, height=300)
     return chart


</code>
"
2362,temporal_aggregation,Show the monthly average PM2.5 for Faridabad in 2017 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Faridabad') & (data['Timestamp'].dt.year == 2017)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Faridabad 2017', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Faridabad') & (data['Timestamp'].dt.year == 2017)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Faridabad 2017', width=450, height=280)


</code>
"
2363,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Himachal Pradesh, Chandigarh, and Sikkim from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Himachal Pradesh', 'Chandigarh', 'Sikkim']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Himachal Pradesh vs Chandigarh vs Sikkim', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Himachal Pradesh', 'Chandigarh', 'Sikkim']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Himachal Pradesh vs Chandigarh vs Sikkim', width=550, height=320)
     return chart


</code>
"
2364,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Mizoram, West Bengal, and Meghalaya across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Mizoram', 'West Bengal', 'Meghalaya']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Mizoram', 'West Bengal', 'Meghalaya']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
2365,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Manipur, Gujarat, and Nagaland from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Manipur', 'Gujarat', 'Nagaland']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Manipur vs Gujarat vs Nagaland', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Manipur', 'Gujarat', 'Nagaland']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Manipur vs Gujarat vs Nagaland', width=550, height=320)
     return chart


</code>
"
2366,specific_pattern,Show a cumulative area chart of PM2.5 readings for Raichur across 2022.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Raichur') & (data['Timestamp'].dt.year == 2022)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Raichur 2022', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Raichur') & (data['Timestamp'].dt.year == 2022)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Raichur 2022', width=600, height=300)
     return chart


</code>
"
2367,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Assam, Meghalaya, and Tamil Nadu across 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Assam', 'Meghalaya', 'Tamil Nadu']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Assam', 'Meghalaya', 'Tamil Nadu']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
     return chart


</code>
"
2368,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Assam stations in 2023, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Assam') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Assam Stations 2023', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Assam') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Assam Stations 2023', width=450, height=350)


</code>
"
2369,temporal_aggregation,Show the monthly average PM2.5 for Kalaburagi in 2024 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Kalaburagi') & (data['Timestamp'].dt.year == 2024)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Kalaburagi 2024', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Kalaburagi') & (data['Timestamp'].dt.year == 2024)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Kalaburagi 2024', width=450, height=280)


</code>
"
2370,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Sikkim, Tamil Nadu, and Himachal Pradesh in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Sikkim', 'Tamil Nadu', 'Himachal Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Sikkim, Tamil Nadu, UP – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Sikkim', 'Tamil Nadu', 'Himachal Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Sikkim, Tamil Nadu, UP – 2022', width=550, height=320)
     return chart


</code>
"
2371,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Andhra Pradesh, Tripura, and Puducherry from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Andhra Pradesh', 'Tripura', 'Puducherry']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Andhra Pradesh vs Tripura vs Puducherry', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Andhra Pradesh', 'Tripura', 'Puducherry']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Andhra Pradesh vs Tripura vs Puducherry', width=550, height=320)
     return chart


</code>
"
2372,temporal_aggregation,Show the monthly average PM2.5 for Bharatpur in 2017 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Bharatpur') & (data['Timestamp'].dt.year == 2017)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Bharatpur 2017', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Bharatpur') & (data['Timestamp'].dt.year == 2017)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Bharatpur 2017', width=450, height=280)


</code>
"
2373,temporal_aggregation,Show the monthly average PM2.5 for Rajgir in 2024 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Rajgir') & (data['Timestamp'].dt.year == 2024)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Rajgir 2024', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Rajgir') & (data['Timestamp'].dt.year == 2024)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Rajgir 2024', width=450, height=280)


</code>
"
2374,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Chandigarh stations in 2023, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Chandigarh') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Chandigarh Stations 2023', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Chandigarh') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Chandigarh Stations 2023', width=450, height=350)


</code>
"
2375,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Tumidih, Tirunelveli, and Pali in 2017 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Tumidih', 'Tirunelveli', 'Pali']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2017)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Tumidih vs Tirunelveli vs Pali – 2017', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Tumidih', 'Tirunelveli', 'Pali']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2017)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Tumidih vs Tirunelveli vs Pali – 2017', width=550, height=320)
     return chart


</code>
"
2376,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Greater Noida, Ghaziabad, and Gangtok in 2017 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Greater Noida', 'Ghaziabad', 'Gangtok']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2017)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Greater Noida vs Ghaziabad vs Gangtok – 2017', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Greater Noida', 'Ghaziabad', 'Gangtok']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2017)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Greater Noida vs Ghaziabad vs Gangtok – 2017', width=550, height=320)
     return chart


</code>
"
2377,specific_pattern,Plot the rolling 30-day average PM2.5 for Assam in 2017 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Assam') & (data['Timestamp'].dt.year == 2017)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Assam 2017', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Assam') & (data['Timestamp'].dt.year == 2017)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Assam 2017', width=600, height=300)


</code>
"
2378,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Sikkim, Gujarat, and Kerala from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Sikkim', 'Gujarat', 'Kerala']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Sikkim vs Gujarat vs Kerala', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Sikkim', 'Gujarat', 'Kerala']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Sikkim vs Gujarat vs Kerala', width=550, height=320)
     return chart


</code>
"
2379,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Latur, Dharwad, and Balasore in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Latur', 'Dharwad', 'Balasore']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Latur vs Dharwad vs Balasore – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Latur', 'Dharwad', 'Balasore']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Latur vs Dharwad vs Balasore – 2022', width=550, height=320)
     return chart


</code>
"
2380,temporal_aggregation,Show the monthly average PM2.5 for Thoothukudi in 2019 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Thoothukudi') & (data['Timestamp'].dt.year == 2019)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Thoothukudi 2019', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Thoothukudi') & (data['Timestamp'].dt.year == 2019)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Thoothukudi 2019', width=450, height=280)


</code>
"
2381,specific_pattern,Plot the rolling 30-day average PM2.5 for Madhya Pradesh in 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Madhya Pradesh') & (data['Timestamp'].dt.year == 2022)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Madhya Pradesh 2022', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Madhya Pradesh') & (data['Timestamp'].dt.year == 2022)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Madhya Pradesh 2022', width=600, height=300)


</code>
"
2382,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Sikkim, Tripura, and Kerala from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Sikkim', 'Tripura', 'Kerala']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Sikkim vs Tripura vs Kerala', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Sikkim', 'Tripura', 'Kerala']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Sikkim vs Tripura vs Kerala', width=550, height=320)
     return chart


</code>
"
2383,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Tripura, Meghalaya, and Telangana from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Tripura', 'Meghalaya', 'Telangana']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Tripura vs Meghalaya vs Telangana', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Tripura', 'Meghalaya', 'Telangana']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Tripura vs Meghalaya vs Telangana', width=550, height=320)
     return chart


</code>
"
2384,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Mizoram, Madhya Pradesh, and Sikkim from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Mizoram', 'Madhya Pradesh', 'Sikkim']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Mizoram vs Madhya Pradesh vs Sikkim', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Mizoram', 'Madhya Pradesh', 'Sikkim']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Mizoram vs Madhya Pradesh vs Sikkim', width=550, height=320)
     return chart


</code>
"
2385,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Puducherry, Bihar, and Uttarakhand across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Puducherry', 'Bihar', 'Uttarakhand']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Puducherry', 'Bihar', 'Uttarakhand']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
2386,specific_pattern,Plot the rolling 30-day average PM2.5 for Puducherry in 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Puducherry') & (data['Timestamp'].dt.year == 2024)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Puducherry 2024', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Puducherry') & (data['Timestamp'].dt.year == 2024)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Puducherry 2024', width=600, height=300)


</code>
"
2387,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Tamil Nadu, Tripura, and Meghalaya across 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Tamil Nadu', 'Tripura', 'Meghalaya']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Tamil Nadu', 'Tripura', 'Meghalaya']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
     return chart


</code>
"
2388,spatial_aggregation,"Show the top 7 states by average PM10 in 2018 as a bar chart, sorted in descending order.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2018]
    df = df.groupby('state')['PM10'].mean().reset_index().dropna()
    df = df.nlargest(7, 'PM10')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Top 7 States by Average PM10 in 2018', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2018]
     df = df.groupby('state')['PM10'].mean().reset_index().dropna()
     df = df.nlargest(7, 'PM10')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Top 7 States by Average PM10 in 2018', width=500, height=300)
     return chart


</code>
"
2389,temporal_aggregation,Show the monthly average PM10 trend for Malegaon from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Malegaon'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Malegaon (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Malegaon'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Malegaon (2019–2024)', width=600, height=300)
     return chart


</code>
"
2390,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Madhya Pradesh, Karnataka, and Maharashtra in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Madhya Pradesh', 'Karnataka', 'Maharashtra']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Madhya Pradesh, Karnataka, UP – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Madhya Pradesh', 'Karnataka', 'Maharashtra']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Madhya Pradesh, Karnataka, UP – 2022', width=550, height=320)
     return chart


</code>
"
2391,temporal_aggregation,Show the monthly average PM10 trend for Thiruvananthapuram from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Thiruvananthapuram'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Thiruvananthapuram (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Thiruvananthapuram'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Thiruvananthapuram (2019–2024)', width=600, height=300)
     return chart


</code>
"
2392,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Punjab, Arunachal Pradesh, and Haryana in 2019 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Punjab', 'Arunachal Pradesh', 'Haryana']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Punjab, Arunachal Pradesh, UP – 2019', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Punjab', 'Arunachal Pradesh', 'Haryana']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Punjab, Arunachal Pradesh, UP – 2019', width=550, height=320)
     return chart


</code>
"
2393,specific_pattern,Plot the rolling 30-day average PM2.5 for Mizoram in 2020 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Mizoram') & (data['Timestamp'].dt.year == 2020)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Mizoram 2020', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Mizoram') & (data['Timestamp'].dt.year == 2020)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Mizoram 2020', width=600, height=300)


</code>
"
2394,temporal_aggregation,Show the monthly average PM10 trend for Nayagarh from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Nayagarh'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Nayagarh (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Nayagarh'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Nayagarh (2019–2024)', width=600, height=300)
     return chart


</code>
"
2395,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Tripura, Delhi, and West Bengal from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Tripura', 'Delhi', 'West Bengal']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Tripura vs Delhi vs West Bengal', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Tripura', 'Delhi', 'West Bengal']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Tripura vs Delhi vs West Bengal', width=550, height=320)
     return chart


</code>
"
2396,temporal_aggregation,Show a monthly bar chart of the number of days Assam exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2020.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Assam') & (data['Timestamp'].dt.year == 2020)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Assam Exceeded WHO PM2.5 Guideline per Month – 2020', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Assam') & (data['Timestamp'].dt.year == 2020)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Assam Exceeded WHO PM2.5 Guideline per Month – 2020', width=500, height=300)
     return chart


</code>
"
2397,specific_pattern,Plot the rolling 30-day average PM2.5 for Uttarakhand in 2023 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Uttarakhand') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Uttarakhand 2023', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Uttarakhand') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Uttarakhand 2023', width=600, height=300)


</code>
"
2398,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Tripura, Gujarat, and Punjab from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Tripura', 'Gujarat', 'Punjab']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Tripura vs Gujarat vs Punjab', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Tripura', 'Gujarat', 'Punjab']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Tripura vs Gujarat vs Punjab', width=550, height=320)
     return chart


</code>
"
2399,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Tripura, Manipur, and West Bengal from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Tripura', 'Manipur', 'West Bengal']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Tripura vs Manipur vs West Bengal', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Tripura', 'Manipur', 'West Bengal']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Tripura vs Manipur vs West Bengal', width=550, height=320)
     return chart


</code>
"
2400,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Amaravati, Lucknow, and Visakhapatnam in 2020 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Amaravati', 'Lucknow', 'Visakhapatnam']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2020)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Amaravati vs Lucknow vs Visakhapatnam – 2020', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Amaravati', 'Lucknow', 'Visakhapatnam']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2020)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Amaravati vs Lucknow vs Visakhapatnam – 2020', width=550, height=320)
     return chart


</code>
"
2401,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Kolkata, Gangtok, and Ahmednagar in 2018 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Kolkata', 'Gangtok', 'Ahmednagar']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2018)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Kolkata vs Gangtok vs Ahmednagar – 2018', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Kolkata', 'Gangtok', 'Ahmednagar']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2018)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Kolkata vs Gangtok vs Ahmednagar – 2018', width=550, height=320)
     return chart


</code>
"
2402,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Arunachal Pradesh, Tamil Nadu, and Assam in 2019 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Arunachal Pradesh', 'Tamil Nadu', 'Assam']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Arunachal Pradesh, Tamil Nadu, UP – 2019', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Arunachal Pradesh', 'Tamil Nadu', 'Assam']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Arunachal Pradesh, Tamil Nadu, UP – 2019', width=550, height=320)
     return chart


</code>
"
2403,spatio_temporal_aggregation,"Create a faceted bar chart showing top 10 states by average PM2.5 per year for 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year.isin([2017,2018,2019,2020])].copy()
    df['Year'] = df['Timestamp'].dt.year
    agg = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    top = agg.groupby('Year').apply(lambda x: x.nlargest(10,'PM2.5')).reset_index(drop=True)
    chart = alt.Chart(top).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Avg PM2.5'),
        y=alt.Y('state:N', sort='-x'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).facet(facet='Year:O', columns=2).properties(title='Top 10 States by PM2.5 per Year')
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year.isin([2017,2018,2019,2020])].copy()
     df['Year'] = df['Timestamp'].dt.year
     agg = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     top = agg.groupby('Year').apply(lambda x: x.nlargest(10,'PM2.5')).reset_index(drop=True)
     chart = alt.Chart(top).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Avg PM2.5'),
         y=alt.Y('state:N', sort='-x'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).facet(facet='Year:O', columns=2).properties(title='Top 10 States by PM2.5 per Year')
     return chart


</code>
"
2404,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Madhya Pradesh, Kerala, and Tripura in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Madhya Pradesh', 'Kerala', 'Tripura']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Madhya Pradesh, Kerala, UP – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Madhya Pradesh', 'Kerala', 'Tripura']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Madhya Pradesh, Kerala, UP – 2024', width=550, height=320)
     return chart


</code>
"
2405,specific_pattern,Show a cumulative area chart of PM2.5 readings for Brajrajnagar across 2024.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Brajrajnagar') & (data['Timestamp'].dt.year == 2024)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Brajrajnagar 2024', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Brajrajnagar') & (data['Timestamp'].dt.year == 2024)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Brajrajnagar 2024', width=600, height=300)
     return chart


</code>
"
2406,temporal_aggregation,Show the monthly average PM10 trend for Virudhunagar from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Virudhunagar'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Virudhunagar (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Virudhunagar'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Virudhunagar (2019–2024)', width=600, height=300)
     return chart


</code>
"
2407,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Arunachal Pradesh, Jammu and Kashmir, and West Bengal across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Arunachal Pradesh', 'Jammu and Kashmir', 'West Bengal']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Arunachal Pradesh', 'Jammu and Kashmir', 'West Bengal']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
2408,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Sikkim stations in 2024, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Sikkim') & (data['Timestamp'].dt.year == 2024)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Sikkim Stations 2024', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Sikkim') & (data['Timestamp'].dt.year == 2024)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Sikkim Stations 2024', width=450, height=350)


</code>
"
2409,temporal_aggregation,Show a monthly bar chart of the number of days Maharashtra exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2018.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Maharashtra') & (data['Timestamp'].dt.year == 2018)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Maharashtra Exceeded WHO PM2.5 Guideline per Month – 2018', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Maharashtra') & (data['Timestamp'].dt.year == 2018)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Maharashtra Exceeded WHO PM2.5 Guideline per Month – 2018', width=500, height=300)
     return chart


</code>
"
2410,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Mizoram, Uttar Pradesh, and Odisha from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Mizoram', 'Uttar Pradesh', 'Odisha']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Mizoram vs Uttar Pradesh vs Odisha', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Mizoram', 'Uttar Pradesh', 'Odisha']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Mizoram vs Uttar Pradesh vs Odisha', width=550, height=320)
     return chart


</code>
"
2411,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Assam, Gujarat, and West Bengal from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Assam', 'Gujarat', 'West Bengal']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Assam vs Gujarat vs West Bengal', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Assam', 'Gujarat', 'West Bengal']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Assam vs Gujarat vs West Bengal', width=550, height=320)
     return chart


</code>
"
2412,temporal_aggregation,Show the monthly average PM10 trend for Lucknow from 2017 to 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Lucknow'].copy()
    df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Lucknow (2017–2022)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Lucknow'].copy()
     df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Lucknow (2017–2022)', width=600, height=300)
     return chart


</code>
"
2413,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Puducherry, Himachal Pradesh, and Bihar across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Puducherry', 'Himachal Pradesh', 'Bihar']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Puducherry', 'Himachal Pradesh', 'Bihar']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
2414,temporal_aggregation,Plot the weekly average PM2.5 for Tirupati in 2017 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Tirupati') & (data['Timestamp'].dt.year == 2017)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Tirupati 2017', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Tirupati') & (data['Timestamp'].dt.year == 2017)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Tirupati 2017', width=600, height=300)
     return chart


</code>
"
2415,temporal_aggregation,Plot the weekly average PM2.5 for Dhule in 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Dhule') & (data['Timestamp'].dt.year == 2024)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Dhule 2024', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Dhule') & (data['Timestamp'].dt.year == 2024)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Dhule 2024', width=600, height=300)
     return chart


</code>
"
2416,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Maharashtra, Haryana, and Chhattisgarh in 2021 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Maharashtra', 'Haryana', 'Chhattisgarh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Maharashtra, Haryana, UP – 2021', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Maharashtra', 'Haryana', 'Chhattisgarh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Maharashtra, Haryana, UP – 2021', width=550, height=320)
     return chart


</code>
"
2417,temporal_aggregation,Plot the weekly average PM2.5 for Vijayawada in 2023 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Vijayawada') & (data['Timestamp'].dt.year == 2023)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Vijayawada 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Vijayawada') & (data['Timestamp'].dt.year == 2023)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Vijayawada 2023', width=600, height=300)
     return chart


</code>
"
2418,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Karnataka, Bihar, and Arunachal Pradesh in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Karnataka', 'Bihar', 'Arunachal Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Karnataka, Bihar, UP – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Karnataka', 'Bihar', 'Arunachal Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Karnataka, Bihar, UP – 2023', width=550, height=320)
     return chart


</code>
"
2419,temporal_aggregation,Plot the weekly average PM2.5 for Fatehabad in 2021 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Fatehabad') & (data['Timestamp'].dt.year == 2021)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Fatehabad 2021', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Fatehabad') & (data['Timestamp'].dt.year == 2021)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Fatehabad 2021', width=600, height=300)
     return chart


</code>
"
2420,spatial_aggregation,"Show the top 7 states by average PM10 in 2020 as a bar chart, sorted in descending order.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2020]
    df = df.groupby('state')['PM10'].mean().reset_index().dropna()
    df = df.nlargest(7, 'PM10')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Top 7 States by Average PM10 in 2020', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2020]
     df = df.groupby('state')['PM10'].mean().reset_index().dropna()
     df = df.nlargest(7, 'PM10')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Top 7 States by Average PM10 in 2020', width=500, height=300)
     return chart


</code>
"
2421,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Puducherry, Mizoram, and Tripura across 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Puducherry', 'Mizoram', 'Tripura']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Puducherry', 'Mizoram', 'Tripura']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
     return chart


</code>
"
2422,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Chandigarh, Meghalaya, and Gujarat in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Chandigarh', 'Meghalaya', 'Gujarat']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Chandigarh, Meghalaya, UP – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Chandigarh', 'Meghalaya', 'Gujarat']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Chandigarh, Meghalaya, UP – 2022', width=550, height=320)
     return chart


</code>
"
2423,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Chhattisgarh, Karnataka, and Tamil Nadu from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Chhattisgarh', 'Karnataka', 'Tamil Nadu']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Chhattisgarh vs Karnataka vs Tamil Nadu', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Chhattisgarh', 'Karnataka', 'Tamil Nadu']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Chhattisgarh vs Karnataka vs Tamil Nadu', width=550, height=320)
     return chart


</code>
"
2424,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Byasanagar, Sonipat, and Jhansi in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Byasanagar', 'Sonipat', 'Jhansi']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Byasanagar vs Sonipat vs Jhansi – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Byasanagar', 'Sonipat', 'Jhansi']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Byasanagar vs Sonipat vs Jhansi – 2024', width=550, height=320)
     return chart


</code>
"
2425,spatial_aggregation,Plot the top 14 states by average PM2.5 in 2018 as a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2018]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nlargest(14, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 14 States by Average PM2.5 in 2018', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2018]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nlargest(14, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 14 States by Average PM2.5 in 2018', width=500, height=300)
     return chart


</code>
"
2426,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Nagaland, Madhya Pradesh, and Madhya Pradesh in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Nagaland', 'Madhya Pradesh', 'Madhya Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Nagaland, Madhya Pradesh, UP – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Nagaland', 'Madhya Pradesh', 'Madhya Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Nagaland, Madhya Pradesh, UP – 2023', width=550, height=320)
     return chart


</code>
"
2427,temporal_aggregation,Show the monthly average PM10 trend for Pali from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Pali'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Pali (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Pali'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Pali (2019–2024)', width=600, height=300)
     return chart


</code>
"
2428,temporal_aggregation,Show the monthly average PM2.5 for Aurangabad in 2018 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Aurangabad') & (data['Timestamp'].dt.year == 2018)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Aurangabad 2018', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Aurangabad') & (data['Timestamp'].dt.year == 2018)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Aurangabad 2018', width=450, height=280)


</code>
"
2429,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Karnataka, Meghalaya, and Uttarakhand in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Karnataka', 'Meghalaya', 'Uttarakhand']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Karnataka, Meghalaya, UP – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Karnataka', 'Meghalaya', 'Uttarakhand']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Karnataka, Meghalaya, UP – 2023', width=550, height=320)
     return chart


</code>
"
2430,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Assam, Odisha, and Telangana in 2021 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Assam', 'Odisha', 'Telangana']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Assam, Odisha, UP – 2021', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Assam', 'Odisha', 'Telangana']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Assam, Odisha, UP – 2021', width=550, height=320)
     return chart


</code>
"
2431,temporal_aggregation,Plot the weekly average PM2.5 for Bhopal in 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Bhopal') & (data['Timestamp'].dt.year == 2024)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Bhopal 2024', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Bhopal') & (data['Timestamp'].dt.year == 2024)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Bhopal 2024', width=600, height=300)
     return chart


</code>
"
2432,temporal_aggregation,Show the monthly average PM2.5 for Karauli in 2020 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Karauli') & (data['Timestamp'].dt.year == 2020)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Karauli 2020', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Karauli') & (data['Timestamp'].dt.year == 2020)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Karauli 2020', width=450, height=280)


</code>
"
2433,temporal_aggregation,Show the monthly average PM2.5 for Patna in 2017 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Patna') & (data['Timestamp'].dt.year == 2017)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Patna 2017', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Patna') & (data['Timestamp'].dt.year == 2017)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Patna 2017', width=450, height=280)


</code>
"
2434,temporal_aggregation,Plot the weekly average PM2.5 for Sonipat in 2023 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Sonipat') & (data['Timestamp'].dt.year == 2023)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Sonipat 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Sonipat') & (data['Timestamp'].dt.year == 2023)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Sonipat 2023', width=600, height=300)
     return chart


</code>
"
2435,spatial_aggregation,Visualize the bottom 7 states with the lowest average PM2.5 in 2020 using a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2020]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nsmallest(7, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Bottom 7 States by Average PM2.5 in 2020', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2020]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nsmallest(7, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Bottom 7 States by Average PM2.5 in 2020', width=500, height=300)
     return chart


</code>
"
2436,temporal_aggregation,Show the monthly average PM2.5 for Haldia in 2022 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Haldia') & (data['Timestamp'].dt.year == 2022)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Haldia 2022', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Haldia') & (data['Timestamp'].dt.year == 2022)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Haldia 2022', width=450, height=280)


</code>
"
2437,temporal_aggregation,Show the monthly average PM2.5 for Muzaffarpur in 2023 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Muzaffarpur') & (data['Timestamp'].dt.year == 2023)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Muzaffarpur 2023', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Muzaffarpur') & (data['Timestamp'].dt.year == 2023)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Muzaffarpur 2023', width=450, height=280)


</code>
"
2438,temporal_aggregation,Show the monthly average PM10 trend for Amaravati from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Amaravati'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Amaravati (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Amaravati'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Amaravati (2019–2024)', width=600, height=300)
     return chart


</code>
"
2439,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Meghalaya, Jharkhand, and Jammu and Kashmir across 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Meghalaya', 'Jharkhand', 'Jammu and Kashmir']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Meghalaya', 'Jharkhand', 'Jammu and Kashmir']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
     return chart


</code>
"
2440,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Jharkhand, Chhattisgarh, and Nagaland across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Jharkhand', 'Chhattisgarh', 'Nagaland']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Jharkhand', 'Chhattisgarh', 'Nagaland']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
2441,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Kerala, Chandigarh, and Gujarat across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Kerala', 'Chandigarh', 'Gujarat']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Kerala', 'Chandigarh', 'Gujarat']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
2442,specific_pattern,Plot the rolling 30-day average PM2.5 for Odisha in 2023 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Odisha') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Odisha 2023', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Odisha') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Odisha 2023', width=600, height=300)


</code>
"
2443,specific_pattern,Plot the rolling 30-day average PM2.5 for Jharkhand in 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Jharkhand') & (data['Timestamp'].dt.year == 2024)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Jharkhand 2024', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Jharkhand') & (data['Timestamp'].dt.year == 2024)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Jharkhand 2024', width=600, height=300)


</code>
"
2444,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Telangana, Maharashtra, and Bihar in 2019 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Telangana', 'Maharashtra', 'Bihar']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Telangana, Maharashtra, UP – 2019', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Telangana', 'Maharashtra', 'Bihar']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Telangana, Maharashtra, UP – 2019', width=550, height=320)
     return chart


</code>
"
2445,temporal_aggregation,Show the monthly average PM10 trend for Sagar from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Sagar'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Sagar (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Sagar'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Sagar (2019–2024)', width=600, height=300)
     return chart


</code>
"
2446,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Bihar, Meghalaya, and Arunachal Pradesh across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Bihar', 'Meghalaya', 'Arunachal Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Bihar', 'Meghalaya', 'Arunachal Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
2447,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Gujarat stations in 2021, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Gujarat') & (data['Timestamp'].dt.year == 2021)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Gujarat Stations 2021', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Gujarat') & (data['Timestamp'].dt.year == 2021)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Gujarat Stations 2021', width=450, height=350)


</code>
"
2448,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Punjab, Madhya Pradesh, and Gujarat in 2017 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Punjab', 'Madhya Pradesh', 'Gujarat']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2017)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Punjab, Madhya Pradesh, UP – 2017', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Punjab', 'Madhya Pradesh', 'Gujarat']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2017)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Punjab, Madhya Pradesh, UP – 2017', width=550, height=320)
     return chart


</code>
"
2449,temporal_aggregation,Show the monthly average PM2.5 for Palwal in 2024 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Palwal') & (data['Timestamp'].dt.year == 2024)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Palwal 2024', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Palwal') & (data['Timestamp'].dt.year == 2024)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Palwal 2024', width=450, height=280)


</code>
"
2450,specific_pattern,Plot the rolling 30-day average PM2.5 for Madhya Pradesh in 2019 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Madhya Pradesh') & (data['Timestamp'].dt.year == 2019)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Madhya Pradesh 2019', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Madhya Pradesh') & (data['Timestamp'].dt.year == 2019)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Madhya Pradesh 2019', width=600, height=300)


</code>
"
2451,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Nagaland, Maharashtra, and Haryana across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Nagaland', 'Maharashtra', 'Haryana']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Nagaland', 'Maharashtra', 'Haryana']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
2452,temporal_aggregation,Show the monthly average PM2.5 for Palwal  in 2022 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Palwal ') & (data['Timestamp'].dt.year == 2022)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Palwal  2022', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Palwal ') & (data['Timestamp'].dt.year == 2022)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Palwal  2022', width=450, height=280)


</code>
"
2453,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Kerala, Chandigarh, and Telangana across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Kerala', 'Chandigarh', 'Telangana']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Kerala', 'Chandigarh', 'Telangana']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
2454,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Himachal Pradesh, Jammu and Kashmir, and West Bengal across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Himachal Pradesh', 'Jammu and Kashmir', 'West Bengal']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Himachal Pradesh', 'Jammu and Kashmir', 'West Bengal']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
2455,spatial_aggregation,"Show the top 6 states by average PM10 in 2024 as a bar chart, sorted in descending order.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2024]
    df = df.groupby('state')['PM10'].mean().reset_index().dropna()
    df = df.nlargest(6, 'PM10')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Top 6 States by Average PM10 in 2024', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2024]
     df = df.groupby('state')['PM10'].mean().reset_index().dropna()
     df = df.nlargest(6, 'PM10')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Top 6 States by Average PM10 in 2024', width=500, height=300)
     return chart


</code>
"
2456,specific_pattern,Show a cumulative area chart of PM2.5 readings for Kanchipuram across 2024.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Kanchipuram') & (data['Timestamp'].dt.year == 2024)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Kanchipuram 2024', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Kanchipuram') & (data['Timestamp'].dt.year == 2024)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Kanchipuram 2024', width=600, height=300)
     return chart


</code>
"
2457,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Odisha, Delhi, and Assam from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Odisha', 'Delhi', 'Assam']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Odisha vs Delhi vs Assam', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Odisha', 'Delhi', 'Assam']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Odisha vs Delhi vs Assam', width=550, height=320)
     return chart


</code>
"
2458,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Nagaland stations in 2020, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Nagaland') & (data['Timestamp'].dt.year == 2020)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Nagaland Stations 2020', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Nagaland') & (data['Timestamp'].dt.year == 2020)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Nagaland Stations 2020', width=450, height=350)


</code>
"
2459,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Telangana, Sikkim, and Chhattisgarh from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Telangana', 'Sikkim', 'Chhattisgarh']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Telangana vs Sikkim vs Chhattisgarh', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Telangana', 'Sikkim', 'Chhattisgarh']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Telangana vs Sikkim vs Chhattisgarh', width=550, height=320)
     return chart


</code>
"
2460,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Telangana stations in 2017, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Telangana') & (data['Timestamp'].dt.year == 2017)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Telangana Stations 2017', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Telangana') & (data['Timestamp'].dt.year == 2017)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Telangana Stations 2017', width=450, height=350)


</code>
"
2461,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Indore, Bhilai, and Varanasi in 2017 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Indore', 'Bhilai', 'Varanasi']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2017)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Indore vs Bhilai vs Varanasi – 2017', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Indore', 'Bhilai', 'Varanasi']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2017)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Indore vs Bhilai vs Varanasi – 2017', width=550, height=320)
     return chart


</code>
"
2462,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Mizoram, Manipur, and Madhya Pradesh from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Mizoram', 'Manipur', 'Madhya Pradesh']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Mizoram vs Manipur vs Madhya Pradesh', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Mizoram', 'Manipur', 'Madhya Pradesh']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Mizoram vs Manipur vs Madhya Pradesh', width=550, height=320)
     return chart


</code>
"
2463,temporal_aggregation,Show the monthly average PM2.5 for Yadgir in 2020 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Yadgir') & (data['Timestamp'].dt.year == 2020)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Yadgir 2020', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Yadgir') & (data['Timestamp'].dt.year == 2020)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Yadgir 2020', width=450, height=280)


</code>
"
2464,temporal_aggregation,Show a monthly bar chart of the number of days Puducherry exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2022.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Puducherry') & (data['Timestamp'].dt.year == 2022)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Puducherry Exceeded WHO PM2.5 Guideline per Month – 2022', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Puducherry') & (data['Timestamp'].dt.year == 2022)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Puducherry Exceeded WHO PM2.5 Guideline per Month – 2022', width=500, height=300)
     return chart


</code>
"
2465,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Madhya Pradesh, Sikkim, and Rajasthan across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Madhya Pradesh', 'Sikkim', 'Rajasthan']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Madhya Pradesh', 'Sikkim', 'Rajasthan']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
2466,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Punjab, Haryana, and Bihar from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Punjab', 'Haryana', 'Bihar']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Punjab vs Haryana vs Bihar', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Punjab', 'Haryana', 'Bihar']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Punjab vs Haryana vs Bihar', width=550, height=320)
     return chart


</code>
"
2467,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Meghalaya, Meghalaya, and Uttar Pradesh across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Meghalaya', 'Meghalaya', 'Uttar Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Meghalaya', 'Meghalaya', 'Uttar Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
2468,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Himachal Pradesh, Maharashtra, and Puducherry in 2018 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Himachal Pradesh', 'Maharashtra', 'Puducherry']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2018)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Himachal Pradesh, Maharashtra, UP – 2018', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Himachal Pradesh', 'Maharashtra', 'Puducherry']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2018)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Himachal Pradesh, Maharashtra, UP – 2018', width=550, height=320)
     return chart


</code>
"
2469,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Jammu and Kashmir, Karnataka, and Rajasthan from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Jammu and Kashmir', 'Karnataka', 'Rajasthan']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Jammu and Kashmir vs Karnataka vs Rajasthan', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Jammu and Kashmir', 'Karnataka', 'Rajasthan']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Jammu and Kashmir vs Karnataka vs Rajasthan', width=550, height=320)
     return chart


</code>
"
2470,specific_pattern,Show a cumulative area chart of PM2.5 readings for Karwar across 2023.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Karwar') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Karwar 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Karwar') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Karwar 2023', width=600, height=300)
     return chart


</code>
"
2471,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Uttarakhand, Haryana, and Nagaland in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Uttarakhand', 'Haryana', 'Nagaland']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Uttarakhand, Haryana, UP – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Uttarakhand', 'Haryana', 'Nagaland']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Uttarakhand, Haryana, UP – 2023', width=550, height=320)
     return chart


</code>
"
2472,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Haryana, Manipur, and Madhya Pradesh across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Haryana', 'Manipur', 'Madhya Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Haryana', 'Manipur', 'Madhya Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
2473,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Kerala, Odisha, and Haryana in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Kerala', 'Odisha', 'Haryana']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Kerala, Odisha, UP – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Kerala', 'Odisha', 'Haryana']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Kerala, Odisha, UP – 2024', width=550, height=320)
     return chart


</code>
"
2474,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Haryana, Karnataka, and Meghalaya from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Haryana', 'Karnataka', 'Meghalaya']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Haryana vs Karnataka vs Meghalaya', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Haryana', 'Karnataka', 'Meghalaya']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Haryana vs Karnataka vs Meghalaya', width=550, height=320)
     return chart


</code>
"
2475,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Nagaland, Uttar Pradesh, and Arunachal Pradesh from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Nagaland', 'Uttar Pradesh', 'Arunachal Pradesh']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Nagaland vs Uttar Pradesh vs Arunachal Pradesh', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Nagaland', 'Uttar Pradesh', 'Arunachal Pradesh']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Nagaland vs Uttar Pradesh vs Arunachal Pradesh', width=550, height=320)
     return chart


</code>
"
2476,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Haryana, Maharashtra, and Karnataka across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Haryana', 'Maharashtra', 'Karnataka']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Haryana', 'Maharashtra', 'Karnataka']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
2477,temporal_aggregation,Show a monthly bar chart of the number of days Tripura exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2024.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Tripura') & (data['Timestamp'].dt.year == 2024)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Tripura Exceeded WHO PM2.5 Guideline per Month – 2024', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Tripura') & (data['Timestamp'].dt.year == 2024)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Tripura Exceeded WHO PM2.5 Guideline per Month – 2024', width=500, height=300)
     return chart


</code>
"
2478,temporal_aggregation,Plot the weekly average PM2.5 for Panipat in 2023 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Panipat') & (data['Timestamp'].dt.year == 2023)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Panipat 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Panipat') & (data['Timestamp'].dt.year == 2023)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Panipat 2023', width=600, height=300)
     return chart


</code>
"
2479,temporal_aggregation,Plot the weekly average PM2.5 for Panipat in 2020 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Panipat') & (data['Timestamp'].dt.year == 2020)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Panipat 2020', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Panipat') & (data['Timestamp'].dt.year == 2020)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Panipat 2020', width=600, height=300)
     return chart


</code>
"
2480,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Haryana, Uttarakhand, and Jammu and Kashmir in 2017 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Haryana', 'Uttarakhand', 'Jammu and Kashmir']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2017)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Haryana, Uttarakhand, UP – 2017', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Haryana', 'Uttarakhand', 'Jammu and Kashmir']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2017)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Haryana, Uttarakhand, UP – 2017', width=550, height=320)
     return chart


</code>
"
2481,specific_pattern,Show a cumulative area chart of PM2.5 readings for Kanpur across 2019.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Kanpur') & (data['Timestamp'].dt.year == 2019)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Kanpur 2019', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Kanpur') & (data['Timestamp'].dt.year == 2019)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Kanpur 2019', width=600, height=300)
     return chart


</code>
"
2482,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Tripura stations in 2020, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Tripura') & (data['Timestamp'].dt.year == 2020)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Tripura Stations 2020', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Tripura') & (data['Timestamp'].dt.year == 2020)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Tripura Stations 2020', width=450, height=350)


</code>
"
2483,temporal_aggregation,Show the monthly average PM10 trend for Udupi from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Udupi'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Udupi (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Udupi'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Udupi (2019–2024)', width=600, height=300)
     return chart


</code>
"
2484,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Karnataka, Mizoram, and Punjab in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Karnataka', 'Mizoram', 'Punjab']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Karnataka, Mizoram, UP – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Karnataka', 'Mizoram', 'Punjab']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Karnataka, Mizoram, UP – 2023', width=550, height=320)
     return chart


</code>
"
2485,specific_pattern,Show a cumulative area chart of PM2.5 readings for Bhiwani across 2024.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Bhiwani') & (data['Timestamp'].dt.year == 2024)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Bhiwani 2024', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Bhiwani') & (data['Timestamp'].dt.year == 2024)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Bhiwani 2024', width=600, height=300)
     return chart


</code>
"
2486,specific_pattern,Plot the rolling 30-day average PM2.5 for Maharashtra in 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Maharashtra') & (data['Timestamp'].dt.year == 2024)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Maharashtra 2024', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Maharashtra') & (data['Timestamp'].dt.year == 2024)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Maharashtra 2024', width=600, height=300)


</code>
"
2487,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Gujarat, Nagaland, and Telangana across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Gujarat', 'Nagaland', 'Telangana']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Gujarat', 'Nagaland', 'Telangana']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
2488,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Gujarat stations in 2019, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Gujarat') & (data['Timestamp'].dt.year == 2019)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Gujarat Stations 2019', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Gujarat') & (data['Timestamp'].dt.year == 2019)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Gujarat Stations 2019', width=450, height=350)


</code>
"
2489,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Odisha stations in 2023, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Odisha') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Odisha Stations 2023', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Odisha') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Odisha Stations 2023', width=450, height=350)


</code>
"
2490,specific_pattern,Plot the rolling 30-day average PM2.5 for Haryana in 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Haryana') & (data['Timestamp'].dt.year == 2024)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Haryana 2024', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Haryana') & (data['Timestamp'].dt.year == 2024)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Haryana 2024', width=600, height=300)


</code>
"
2491,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Arunachal Pradesh, Arunachal Pradesh, and Kerala across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Arunachal Pradesh', 'Arunachal Pradesh', 'Kerala']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Arunachal Pradesh', 'Arunachal Pradesh', 'Kerala']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
2492,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Chhattisgarh, Telangana, and Andhra Pradesh across 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Chhattisgarh', 'Telangana', 'Andhra Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Chhattisgarh', 'Telangana', 'Andhra Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
     return chart


</code>
"
2493,temporal_aggregation,Show the monthly average PM2.5 for Surat in 2024 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Surat') & (data['Timestamp'].dt.year == 2024)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Surat 2024', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Surat') & (data['Timestamp'].dt.year == 2024)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Surat 2024', width=450, height=280)


</code>
"
2494,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Gujarat, Tamil Nadu, and Tripura across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Gujarat', 'Tamil Nadu', 'Tripura']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Gujarat', 'Tamil Nadu', 'Tripura']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
2495,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Uttarakhand, Andhra Pradesh, and Arunachal Pradesh from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Uttarakhand', 'Andhra Pradesh', 'Arunachal Pradesh']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Uttarakhand vs Andhra Pradesh vs Arunachal Pradesh', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Uttarakhand', 'Andhra Pradesh', 'Arunachal Pradesh']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Uttarakhand vs Andhra Pradesh vs Arunachal Pradesh', width=550, height=320)
     return chart


</code>
"
2496,specific_pattern,Show a cumulative area chart of PM2.5 readings for Prayagraj across 2023.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Prayagraj') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Prayagraj 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Prayagraj') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Prayagraj 2023', width=600, height=300)
     return chart


</code>
"
2497,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Bareilly, Nashik, and Thiruvananthapuram in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Bareilly', 'Nashik', 'Thiruvananthapuram']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Bareilly vs Nashik vs Thiruvananthapuram – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Bareilly', 'Nashik', 'Thiruvananthapuram']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Bareilly vs Nashik vs Thiruvananthapuram – 2022', width=550, height=320)
     return chart


</code>
"
2498,temporal_aggregation,Show the monthly average PM10 trend for Bhagalpur from 2017 to 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Bhagalpur'].copy()
    df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Bhagalpur (2017–2022)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Bhagalpur'].copy()
     df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Bhagalpur (2017–2022)', width=600, height=300)
     return chart


</code>
"
2499,temporal_aggregation,Show the monthly average PM10 trend for Kadapa from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Kadapa'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Kadapa (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Kadapa'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Kadapa (2019–2024)', width=600, height=300)
     return chart


</code>
"
2500,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Chandigarh, Bihar, and Rajasthan across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Chandigarh', 'Bihar', 'Rajasthan']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Chandigarh', 'Bihar', 'Rajasthan']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
2501,temporal_aggregation,Show a monthly bar chart of the number of days Delhi exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2017.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Delhi') & (data['Timestamp'].dt.year == 2017)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Delhi Exceeded WHO PM2.5 Guideline per Month – 2017', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Delhi') & (data['Timestamp'].dt.year == 2017)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Delhi Exceeded WHO PM2.5 Guideline per Month – 2017', width=500, height=300)
     return chart


</code>
"
2502,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Rajasthan, Uttar Pradesh, and Sikkim from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Rajasthan', 'Uttar Pradesh', 'Sikkim']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Rajasthan vs Uttar Pradesh vs Sikkim', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Rajasthan', 'Uttar Pradesh', 'Sikkim']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Rajasthan vs Uttar Pradesh vs Sikkim', width=550, height=320)
     return chart


</code>
"
2503,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Jharkhand, Punjab, and Jharkhand from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Jharkhand', 'Punjab', 'Jharkhand']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Jharkhand vs Punjab vs Jharkhand', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Jharkhand', 'Punjab', 'Jharkhand']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Jharkhand vs Punjab vs Jharkhand', width=550, height=320)
     return chart


</code>
"
2504,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Ariyalur, Bahadurgarh, and Kalyan in 2019 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Ariyalur', 'Bahadurgarh', 'Kalyan']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Ariyalur vs Bahadurgarh vs Kalyan – 2019', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Ariyalur', 'Bahadurgarh', 'Kalyan']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Ariyalur vs Bahadurgarh vs Kalyan – 2019', width=550, height=320)
     return chart


</code>
"
2505,temporal_aggregation,Show the monthly average PM2.5 for Bettiah in 2024 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Bettiah') & (data['Timestamp'].dt.year == 2024)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Bettiah 2024', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Bettiah') & (data['Timestamp'].dt.year == 2024)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Bettiah 2024', width=450, height=280)


</code>
"
2506,temporal_aggregation,Show a monthly bar chart of the number of days Assam exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2024.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Assam') & (data['Timestamp'].dt.year == 2024)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Assam Exceeded WHO PM2.5 Guideline per Month – 2024', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Assam') & (data['Timestamp'].dt.year == 2024)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Assam Exceeded WHO PM2.5 Guideline per Month – 2024', width=500, height=300)
     return chart


</code>
"
2507,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Haryana, Arunachal Pradesh, and Jammu and Kashmir in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Haryana', 'Arunachal Pradesh', 'Jammu and Kashmir']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Haryana, Arunachal Pradesh, UP – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Haryana', 'Arunachal Pradesh', 'Jammu and Kashmir']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Haryana, Arunachal Pradesh, UP – 2024', width=550, height=320)
     return chart


</code>
"
2508,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Jammu and Kashmir, Meghalaya, and Rajasthan from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Jammu and Kashmir', 'Meghalaya', 'Rajasthan']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Jammu and Kashmir vs Meghalaya vs Rajasthan', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Jammu and Kashmir', 'Meghalaya', 'Rajasthan']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Jammu and Kashmir vs Meghalaya vs Rajasthan', width=550, height=320)
     return chart


</code>
"
2509,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Jammu and Kashmir, Gujarat, and Tripura from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Jammu and Kashmir', 'Gujarat', 'Tripura']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Jammu and Kashmir vs Gujarat vs Tripura', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Jammu and Kashmir', 'Gujarat', 'Tripura']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Jammu and Kashmir vs Gujarat vs Tripura', width=550, height=320)
     return chart


</code>
"
2510,specific_pattern,Show a cumulative area chart of PM2.5 readings for Bileipada across 2024.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Bileipada') & (data['Timestamp'].dt.year == 2024)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Bileipada 2024', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Bileipada') & (data['Timestamp'].dt.year == 2024)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Bileipada 2024', width=600, height=300)
     return chart


</code>
"
2511,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Kadapa, Jorapokhar, and Kadapa in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Kadapa', 'Jorapokhar', 'Kadapa']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Kadapa vs Jorapokhar vs Kadapa – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Kadapa', 'Jorapokhar', 'Kadapa']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Kadapa vs Jorapokhar vs Kadapa – 2023', width=550, height=320)
     return chart


</code>
"
2512,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Telangana, Delhi, and Meghalaya across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Telangana', 'Delhi', 'Meghalaya']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Telangana', 'Delhi', 'Meghalaya']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
2513,spatial_aggregation,Plot the top 15 states by average PM2.5 in 2023 as a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2023]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nlargest(15, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 15 States by Average PM2.5 in 2023', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2023]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nlargest(15, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 15 States by Average PM2.5 in 2023', width=500, height=300)
     return chart


</code>
"
2514,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Bihar, Nagaland, and Chandigarh in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Bihar', 'Nagaland', 'Chandigarh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Bihar, Nagaland, UP – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Bihar', 'Nagaland', 'Chandigarh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Bihar, Nagaland, UP – 2023', width=550, height=320)
     return chart


</code>
"
2515,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Prayagraj, Narnaul, and Udupi in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Prayagraj', 'Narnaul', 'Udupi']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Prayagraj vs Narnaul vs Udupi – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Prayagraj', 'Narnaul', 'Udupi']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Prayagraj vs Narnaul vs Udupi – 2024', width=550, height=320)
     return chart


</code>
"
2516,specific_pattern,Show a cumulative area chart of PM2.5 readings for Baripada across 2022.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Baripada') & (data['Timestamp'].dt.year == 2022)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Baripada 2022', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Baripada') & (data['Timestamp'].dt.year == 2022)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Baripada 2022', width=600, height=300)
     return chart


</code>
"
2517,temporal_aggregation,Show the monthly average PM10 trend for Bileipada from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Bileipada'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Bileipada (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Bileipada'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Bileipada (2019–2024)', width=600, height=300)
     return chart


</code>
"
2518,specific_pattern,Plot the rolling 30-day average PM2.5 for Haryana in 2018 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Haryana') & (data['Timestamp'].dt.year == 2018)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Haryana 2018', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Haryana') & (data['Timestamp'].dt.year == 2018)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Haryana 2018', width=600, height=300)


</code>
"
2519,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Mizoram, Chhattisgarh, and Madhya Pradesh from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Mizoram', 'Chhattisgarh', 'Madhya Pradesh']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Mizoram vs Chhattisgarh vs Madhya Pradesh', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Mizoram', 'Chhattisgarh', 'Madhya Pradesh']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Mizoram vs Chhattisgarh vs Madhya Pradesh', width=550, height=320)
     return chart


</code>
"
2520,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Bihar, Tamil Nadu, and Chandigarh in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Bihar', 'Tamil Nadu', 'Chandigarh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Bihar, Tamil Nadu, UP – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Bihar', 'Tamil Nadu', 'Chandigarh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Bihar, Tamil Nadu, UP – 2022', width=550, height=320)
     return chart


</code>
"
2521,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Chhattisgarh, Andhra Pradesh, and Uttar Pradesh from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Chhattisgarh', 'Andhra Pradesh', 'Uttar Pradesh']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Chhattisgarh vs Andhra Pradesh vs Uttar Pradesh', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Chhattisgarh', 'Andhra Pradesh', 'Uttar Pradesh']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Chhattisgarh vs Andhra Pradesh vs Uttar Pradesh', width=550, height=320)
     return chart


</code>
"
2522,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Bihar, Uttarakhand, and Arunachal Pradesh across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Bihar', 'Uttarakhand', 'Arunachal Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Bihar', 'Uttarakhand', 'Arunachal Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
2523,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Uttarakhand, Himachal Pradesh, and Jammu and Kashmir in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Uttarakhand', 'Himachal Pradesh', 'Jammu and Kashmir']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Uttarakhand, Himachal Pradesh, UP – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Uttarakhand', 'Himachal Pradesh', 'Jammu and Kashmir']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Uttarakhand, Himachal Pradesh, UP – 2022', width=550, height=320)
     return chart


</code>
"
2524,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Assam, West Bengal, and Mizoram in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Assam', 'West Bengal', 'Mizoram']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Assam, West Bengal, UP – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Assam', 'West Bengal', 'Mizoram']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Assam, West Bengal, UP – 2023', width=550, height=320)
     return chart


</code>
"
2525,spatial_aggregation,"Show the top 11 states by average PM10 in 2021 as a bar chart, sorted in descending order.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2021]
    df = df.groupby('state')['PM10'].mean().reset_index().dropna()
    df = df.nlargest(11, 'PM10')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Top 11 States by Average PM10 in 2021', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2021]
     df = df.groupby('state')['PM10'].mean().reset_index().dropna()
     df = df.nlargest(11, 'PM10')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Top 11 States by Average PM10 in 2021', width=500, height=300)
     return chart


</code>
"
2526,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Kerala, Assam, and Haryana across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Kerala', 'Assam', 'Haryana']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Kerala', 'Assam', 'Haryana']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
2527,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Himachal Pradesh, Tamil Nadu, and Andhra Pradesh from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Himachal Pradesh', 'Tamil Nadu', 'Andhra Pradesh']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Himachal Pradesh vs Tamil Nadu vs Andhra Pradesh', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Himachal Pradesh', 'Tamil Nadu', 'Andhra Pradesh']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Himachal Pradesh vs Tamil Nadu vs Andhra Pradesh', width=550, height=320)
     return chart


</code>
"
2528,temporal_aggregation,Show the monthly average PM2.5 for Pimpri-Chinchwad in 2020 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Pimpri-Chinchwad') & (data['Timestamp'].dt.year == 2020)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Pimpri-Chinchwad 2020', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Pimpri-Chinchwad') & (data['Timestamp'].dt.year == 2020)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Pimpri-Chinchwad 2020', width=450, height=280)


</code>
"
2529,specific_pattern,Show a cumulative area chart of PM2.5 readings for Jabalpur across 2022.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Jabalpur') & (data['Timestamp'].dt.year == 2022)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Jabalpur 2022', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Jabalpur') & (data['Timestamp'].dt.year == 2022)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Jabalpur 2022', width=600, height=300)
     return chart


</code>
"
2530,temporal_aggregation,Show the monthly average PM10 trend for Bhiwadi from 2017 to 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Bhiwadi'].copy()
    df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Bhiwadi (2017–2022)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Bhiwadi'].copy()
     df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Bhiwadi (2017–2022)', width=600, height=300)
     return chart


</code>
"
2531,spatial_aggregation,Plot the top 14 states by average PM2.5 in 2024 as a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2024]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nlargest(14, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 14 States by Average PM2.5 in 2024', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2024]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nlargest(14, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 14 States by Average PM2.5 in 2024', width=500, height=300)
     return chart


</code>
"
2532,temporal_aggregation,Show the monthly average PM10 trend for Kishanganj from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Kishanganj'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Kishanganj (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Kishanganj'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Kishanganj (2019–2024)', width=600, height=300)
     return chart


</code>
"
2533,specific_pattern,Plot the rolling 30-day average PM2.5 for Telangana in 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Telangana') & (data['Timestamp'].dt.year == 2024)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Telangana 2024', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Telangana') & (data['Timestamp'].dt.year == 2024)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Telangana 2024', width=600, height=300)


</code>
"
2534,temporal_aggregation,Show the monthly average PM10 trend for Kalaburagi from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Kalaburagi'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Kalaburagi (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Kalaburagi'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Kalaburagi (2019–2024)', width=600, height=300)
     return chart


</code>
"
2535,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Chhal, Kalaburagi, and Muzaffarpur in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Chhal', 'Kalaburagi', 'Muzaffarpur']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Chhal vs Kalaburagi vs Muzaffarpur – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Chhal', 'Kalaburagi', 'Muzaffarpur']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Chhal vs Kalaburagi vs Muzaffarpur – 2023', width=550, height=320)
     return chart


</code>
"
2536,spatial_aggregation,Show a bar chart of the top 11 cities by median PM2.5 in 2023.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2023]
    df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
    df = df.nlargest(11, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
        y=alt.Y('city:N', sort='-x', title='City'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
        tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 11 Cities by Median PM2.5 in 2023', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2023]
     df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
     df = df.nlargest(11, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
         y=alt.Y('city:N', sort='-x', title='City'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
         tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 11 Cities by Median PM2.5 in 2023', width=500, height=300)
     return chart


</code>
"
2537,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Uttarakhand, Uttar Pradesh, and Punjab from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Uttarakhand', 'Uttar Pradesh', 'Punjab']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Uttarakhand vs Uttar Pradesh vs Punjab', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Uttarakhand', 'Uttar Pradesh', 'Punjab']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Uttarakhand vs Uttar Pradesh vs Punjab', width=550, height=320)
     return chart


</code>
"
2538,temporal_aggregation,Plot the weekly average PM2.5 for Chikkaballapur in 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Chikkaballapur') & (data['Timestamp'].dt.year == 2024)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Chikkaballapur 2024', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Chikkaballapur') & (data['Timestamp'].dt.year == 2024)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Chikkaballapur 2024', width=600, height=300)
     return chart


</code>
"
2539,temporal_aggregation,Show a monthly bar chart of the number of days Haryana exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2023.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Haryana') & (data['Timestamp'].dt.year == 2023)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Haryana Exceeded WHO PM2.5 Guideline per Month – 2023', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Haryana') & (data['Timestamp'].dt.year == 2023)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Haryana Exceeded WHO PM2.5 Guideline per Month – 2023', width=500, height=300)
     return chart


</code>
"
2540,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Karnataka stations in 2017, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Karnataka') & (data['Timestamp'].dt.year == 2017)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Karnataka Stations 2017', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Karnataka') & (data['Timestamp'].dt.year == 2017)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Karnataka Stations 2017', width=450, height=350)


</code>
"
2541,specific_pattern,Show a cumulative area chart of PM2.5 readings for Bareilly across 2024.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Bareilly') & (data['Timestamp'].dt.year == 2024)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Bareilly 2024', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Bareilly') & (data['Timestamp'].dt.year == 2024)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Bareilly 2024', width=600, height=300)
     return chart


</code>
"
2542,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Rajasthan, Arunachal Pradesh, and Uttar Pradesh across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Rajasthan', 'Arunachal Pradesh', 'Uttar Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Rajasthan', 'Arunachal Pradesh', 'Uttar Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
2543,temporal_aggregation,Show the monthly average PM2.5 for Ariyalur in 2017 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Ariyalur') & (data['Timestamp'].dt.year == 2017)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Ariyalur 2017', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Ariyalur') & (data['Timestamp'].dt.year == 2017)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Ariyalur 2017', width=450, height=280)


</code>
"
2544,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Puducherry, West Bengal, and Andhra Pradesh across 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Puducherry', 'West Bengal', 'Andhra Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Puducherry', 'West Bengal', 'Andhra Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
     return chart


</code>
"
2545,specific_pattern,Show a cumulative area chart of PM2.5 readings for Bhiwadi across 2024.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Bhiwadi') & (data['Timestamp'].dt.year == 2024)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Bhiwadi 2024', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Bhiwadi') & (data['Timestamp'].dt.year == 2024)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Bhiwadi 2024', width=600, height=300)
     return chart


</code>
"
2546,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Tamil Nadu, Chandigarh, and Odisha from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Tamil Nadu', 'Chandigarh', 'Odisha']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Tamil Nadu vs Chandigarh vs Odisha', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Tamil Nadu', 'Chandigarh', 'Odisha']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Tamil Nadu vs Chandigarh vs Odisha', width=550, height=320)
     return chart


</code>
"
2547,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Chandigarh stations in 2019, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Chandigarh') & (data['Timestamp'].dt.year == 2019)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Chandigarh Stations 2019', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Chandigarh') & (data['Timestamp'].dt.year == 2019)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Chandigarh Stations 2019', width=450, height=350)


</code>
"
2548,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Puducherry, Assam, and Chhattisgarh from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Puducherry', 'Assam', 'Chhattisgarh']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Puducherry vs Assam vs Chhattisgarh', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Puducherry', 'Assam', 'Chhattisgarh']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Puducherry vs Assam vs Chhattisgarh', width=550, height=320)
     return chart


</code>
"
2549,temporal_aggregation,Plot the weekly average PM2.5 for Mumbai in 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Mumbai') & (data['Timestamp'].dt.year == 2024)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Mumbai 2024', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Mumbai') & (data['Timestamp'].dt.year == 2024)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Mumbai 2024', width=600, height=300)
     return chart


</code>
"
2550,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Uttarakhand, Tripura, and Chandigarh in 2019 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Uttarakhand', 'Tripura', 'Chandigarh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Uttarakhand, Tripura, UP – 2019', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Uttarakhand', 'Tripura', 'Chandigarh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Uttarakhand, Tripura, UP – 2019', width=550, height=320)
     return chart


</code>
"
2551,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Arunachal Pradesh stations in 2021, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Arunachal Pradesh') & (data['Timestamp'].dt.year == 2021)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Arunachal Pradesh Stations 2021', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Arunachal Pradesh') & (data['Timestamp'].dt.year == 2021)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Arunachal Pradesh Stations 2021', width=450, height=350)


</code>
"
2552,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Rajasthan, Madhya Pradesh, and Kerala across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Rajasthan', 'Madhya Pradesh', 'Kerala']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Rajasthan', 'Madhya Pradesh', 'Kerala']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
2553,temporal_aggregation,Show the monthly average PM2.5 for Vatva in 2022 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Vatva') & (data['Timestamp'].dt.year == 2022)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Vatva 2022', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Vatva') & (data['Timestamp'].dt.year == 2022)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Vatva 2022', width=450, height=280)


</code>
"
2554,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Punjab, Uttar Pradesh, and Haryana from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Punjab', 'Uttar Pradesh', 'Haryana']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Punjab vs Uttar Pradesh vs Haryana', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Punjab', 'Uttar Pradesh', 'Haryana']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Punjab vs Uttar Pradesh vs Haryana', width=550, height=320)
     return chart


</code>
"
2555,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Karnataka, Tamil Nadu, and Uttarakhand from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Karnataka', 'Tamil Nadu', 'Uttarakhand']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Karnataka vs Tamil Nadu vs Uttarakhand', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Karnataka', 'Tamil Nadu', 'Uttarakhand']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Karnataka vs Tamil Nadu vs Uttarakhand', width=550, height=320)
     return chart


</code>
"
2556,spatial_aggregation,Plot the distribution of PM2.5 values in Tripura across all years using a histogram.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['state'] == 'Tripura'].dropna(subset=['PM2.5'])
    df = df[['PM2.5']]
    chart = alt.Chart(df).mark_bar(color='steelblue', opacity=0.75).encode(
        x=alt.X('PM2\.5:Q', bin=alt.Bin(maxbins=40), title='PM2.5 (µg/m³)'),
        y=alt.Y('count()', title='Number of Observations'),
        tooltip=[alt.Tooltip('PM2\.5:Q', bin=True), 'count()']
    ).properties(title='PM2.5 Distribution – Tripura (All Years)', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['state'] == 'Tripura'].dropna(subset=['PM2.5'])
     df = df[['PM2.5']]
     chart = alt.Chart(df).mark_bar(color='steelblue', opacity=0.75).encode(
         x=alt.X('PM2\.5:Q', bin=alt.Bin(maxbins=40), title='PM2.5 (µg/m³)'),
         y=alt.Y('count()', title='Number of Observations'),
         tooltip=[alt.Tooltip('PM2\.5:Q', bin=True), 'count()']
     ).properties(title='PM2.5 Distribution – Tripura (All Years)', width=500, height=300)
     return chart


</code>
"
2557,temporal_aggregation,Plot the weekly average PM2.5 for Ernakulam in 2021 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Ernakulam') & (data['Timestamp'].dt.year == 2021)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Ernakulam 2021', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Ernakulam') & (data['Timestamp'].dt.year == 2021)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Ernakulam 2021', width=600, height=300)
     return chart


</code>
"
2558,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Jammu and Kashmir, Uttarakhand, and Telangana from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Jammu and Kashmir', 'Uttarakhand', 'Telangana']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Jammu and Kashmir vs Uttarakhand vs Telangana', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Jammu and Kashmir', 'Uttarakhand', 'Telangana']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Jammu and Kashmir vs Uttarakhand vs Telangana', width=550, height=320)
     return chart


</code>
"
2559,temporal_aggregation,Show a monthly bar chart of the number of days Kerala exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2024.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Kerala') & (data['Timestamp'].dt.year == 2024)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Kerala Exceeded WHO PM2.5 Guideline per Month – 2024', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Kerala') & (data['Timestamp'].dt.year == 2024)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Kerala Exceeded WHO PM2.5 Guideline per Month – 2024', width=500, height=300)
     return chart


</code>
"
2560,specific_pattern,Plot the rolling 30-day average PM2.5 for Mizoram in 2023 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Mizoram') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Mizoram 2023', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Mizoram') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Mizoram 2023', width=600, height=300)


</code>
"
2561,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Rairangpur, Ariyalur, and Mandi Gobindgarh in 2020 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Rairangpur', 'Ariyalur', 'Mandi Gobindgarh']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2020)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Rairangpur vs Ariyalur vs Mandi Gobindgarh – 2020', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Rairangpur', 'Ariyalur', 'Mandi Gobindgarh']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2020)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Rairangpur vs Ariyalur vs Mandi Gobindgarh – 2020', width=550, height=320)
     return chart


</code>
"
2562,temporal_aggregation,Show the monthly average PM10 trend for Vellore from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Vellore'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Vellore (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Vellore'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Vellore (2019–2024)', width=600, height=300)
     return chart


</code>
"
2563,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Puducherry, Telangana, and Sikkim from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Puducherry', 'Telangana', 'Sikkim']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Puducherry vs Telangana vs Sikkim', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Puducherry', 'Telangana', 'Sikkim']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Puducherry vs Telangana vs Sikkim', width=550, height=320)
     return chart


</code>
"
2564,specific_pattern,Show a cumulative area chart of PM2.5 readings for Byrnihat across 2022.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Byrnihat') & (data['Timestamp'].dt.year == 2022)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Byrnihat 2022', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Byrnihat') & (data['Timestamp'].dt.year == 2022)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Byrnihat 2022', width=600, height=300)
     return chart


</code>
"
2565,spatial_aggregation,Plot the top 10 states by average PM2.5 in 2019 as a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2019]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nlargest(10, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 10 States by Average PM2.5 in 2019', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2019]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nlargest(10, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 10 States by Average PM2.5 in 2019', width=500, height=300)
     return chart


</code>
"
2566,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Manipur, Punjab, and Nagaland across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Manipur', 'Punjab', 'Nagaland']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Manipur', 'Punjab', 'Nagaland']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
2567,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Assam, Karnataka, and Meghalaya across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Assam', 'Karnataka', 'Meghalaya']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Assam', 'Karnataka', 'Meghalaya']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
2568,temporal_aggregation,Show the monthly average PM10 trend for Gangtok from 2017 to 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Gangtok'].copy()
    df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Gangtok (2017–2022)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Gangtok'].copy()
     df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Gangtok (2017–2022)', width=600, height=300)
     return chart


</code>
"
2569,temporal_aggregation,Plot the weekly average PM2.5 for Kota in 2020 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Kota') & (data['Timestamp'].dt.year == 2020)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Kota 2020', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Kota') & (data['Timestamp'].dt.year == 2020)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Kota 2020', width=600, height=300)
     return chart


</code>
"
2570,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Katihar, Faridabad, and Nayagarh in 2018 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Katihar', 'Faridabad', 'Nayagarh']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2018)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Katihar vs Faridabad vs Nayagarh – 2018', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Katihar', 'Faridabad', 'Nayagarh']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2018)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Katihar vs Faridabad vs Nayagarh – 2018', width=550, height=320)
     return chart


</code>
"
2571,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Tamil Nadu, Himachal Pradesh, and Gujarat from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Tamil Nadu', 'Himachal Pradesh', 'Gujarat']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Tamil Nadu vs Himachal Pradesh vs Gujarat', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Tamil Nadu', 'Himachal Pradesh', 'Gujarat']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Tamil Nadu vs Himachal Pradesh vs Gujarat', width=550, height=320)
     return chart


</code>
"
2572,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Tamil Nadu, Nagaland, and Chandigarh from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Tamil Nadu', 'Nagaland', 'Chandigarh']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Tamil Nadu vs Nagaland vs Chandigarh', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Tamil Nadu', 'Nagaland', 'Chandigarh']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Tamil Nadu vs Nagaland vs Chandigarh', width=550, height=320)
     return chart


</code>
"
2573,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Chhattisgarh, Arunachal Pradesh, and Karnataka across 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Chhattisgarh', 'Arunachal Pradesh', 'Karnataka']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Chhattisgarh', 'Arunachal Pradesh', 'Karnataka']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
     return chart


</code>
"
2574,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for West Bengal, Gujarat, and Telangana from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['West Bengal', 'Gujarat', 'Telangana']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: West Bengal vs Gujarat vs Telangana', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['West Bengal', 'Gujarat', 'Telangana']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: West Bengal vs Gujarat vs Telangana', width=550, height=320)
     return chart


</code>
"
2575,specific_pattern,Show a cumulative area chart of PM2.5 readings for Charkhi Dadri across 2022.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Charkhi Dadri') & (data['Timestamp'].dt.year == 2022)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Charkhi Dadri 2022', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Charkhi Dadri') & (data['Timestamp'].dt.year == 2022)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Charkhi Dadri 2022', width=600, height=300)
     return chart


</code>
"
2576,temporal_aggregation,Show a monthly bar chart of the number of days Punjab exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2018.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Punjab') & (data['Timestamp'].dt.year == 2018)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Punjab Exceeded WHO PM2.5 Guideline per Month – 2018', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Punjab') & (data['Timestamp'].dt.year == 2018)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Punjab Exceeded WHO PM2.5 Guideline per Month – 2018', width=500, height=300)
     return chart


</code>
"
2577,temporal_aggregation,Show the monthly average PM2.5 for Dharwad in 2020 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Dharwad') & (data['Timestamp'].dt.year == 2020)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Dharwad 2020', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Dharwad') & (data['Timestamp'].dt.year == 2020)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Dharwad 2020', width=450, height=280)


</code>
"
2578,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Sikkim, Manipur, and Bihar across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Sikkim', 'Manipur', 'Bihar']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Sikkim', 'Manipur', 'Bihar']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
2579,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Meghalaya, Jammu and Kashmir, and Jharkhand across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Meghalaya', 'Jammu and Kashmir', 'Jharkhand']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Meghalaya', 'Jammu and Kashmir', 'Jharkhand']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
2580,temporal_aggregation,Show a monthly bar chart of the number of days Punjab exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2023.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Punjab') & (data['Timestamp'].dt.year == 2023)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Punjab Exceeded WHO PM2.5 Guideline per Month – 2023', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Punjab') & (data['Timestamp'].dt.year == 2023)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Punjab Exceeded WHO PM2.5 Guideline per Month – 2023', width=500, height=300)
     return chart


</code>
"
2581,spatial_aggregation,Visualize the bottom 5 states with the lowest average PM2.5 in 2023 using a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2023]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nsmallest(5, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Bottom 5 States by Average PM2.5 in 2023', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2023]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nsmallest(5, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='greens'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Bottom 5 States by Average PM2.5 in 2023', width=500, height=300)
     return chart


</code>
"
2582,temporal_aggregation,Show the monthly average PM10 trend for Shillong from 2017 to 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Shillong'].copy()
    df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Shillong (2017–2022)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Shillong'].copy()
     df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Shillong (2017–2022)', width=600, height=300)
     return chart


</code>
"
2583,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Chandigarh, West Bengal, and Puducherry from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Chandigarh', 'West Bengal', 'Puducherry']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Chandigarh vs West Bengal vs Puducherry', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Chandigarh', 'West Bengal', 'Puducherry']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Chandigarh vs West Bengal vs Puducherry', width=550, height=320)
     return chart


</code>
"
2584,temporal_aggregation,Plot the weekly average PM2.5 for Chhal in 2023 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Chhal') & (data['Timestamp'].dt.year == 2023)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Chhal 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Chhal') & (data['Timestamp'].dt.year == 2023)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Chhal 2023', width=600, height=300)
     return chart


</code>
"
2585,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Mizoram stations in 2019, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Mizoram') & (data['Timestamp'].dt.year == 2019)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Mizoram Stations 2019', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Mizoram') & (data['Timestamp'].dt.year == 2019)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Mizoram Stations 2019', width=450, height=350)


</code>
"
2586,temporal_aggregation,Show the monthly average PM2.5 for Ajmer in 2023 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Ajmer') & (data['Timestamp'].dt.year == 2023)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Ajmer 2023', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Ajmer') & (data['Timestamp'].dt.year == 2023)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Ajmer 2023', width=450, height=280)


</code>
"
2587,specific_pattern,Plot the rolling 30-day average PM2.5 for Rajasthan in 2018 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Rajasthan') & (data['Timestamp'].dt.year == 2018)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Rajasthan 2018', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Rajasthan') & (data['Timestamp'].dt.year == 2018)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Rajasthan 2018', width=600, height=300)


</code>
"
2588,temporal_aggregation,Show the monthly average PM10 trend for Jalandhar from 2017 to 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Jalandhar'].copy()
    df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Jalandhar (2017–2022)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Jalandhar'].copy()
     df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Jalandhar (2017–2022)', width=600, height=300)
     return chart


</code>
"
2589,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Barbil, Agra, and Gaya in 2020 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Barbil', 'Agra', 'Gaya']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2020)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Barbil vs Agra vs Gaya – 2020', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Barbil', 'Agra', 'Gaya']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2020)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Barbil vs Agra vs Gaya – 2020', width=550, height=320)
     return chart


</code>
"
2590,spatial_aggregation,Show a bar chart of the top 5 cities by median PM2.5 in 2023.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2023]
    df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
    df = df.nlargest(5, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
        y=alt.Y('city:N', sort='-x', title='City'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
        tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 5 Cities by Median PM2.5 in 2023', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2023]
     df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
     df = df.nlargest(5, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
         y=alt.Y('city:N', sort='-x', title='City'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
         tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 5 Cities by Median PM2.5 in 2023', width=500, height=300)
     return chart


</code>
"
2591,temporal_aggregation,Show a monthly bar chart of the number of days Assam exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2022.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Assam') & (data['Timestamp'].dt.year == 2022)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Assam Exceeded WHO PM2.5 Guideline per Month – 2022', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Assam') & (data['Timestamp'].dt.year == 2022)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Assam Exceeded WHO PM2.5 Guideline per Month – 2022', width=500, height=300)
     return chart


</code>
"
2592,temporal_aggregation,Show the monthly average PM2.5 for Pimpri-Chinchwad in 2017 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Pimpri-Chinchwad') & (data['Timestamp'].dt.year == 2017)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Pimpri-Chinchwad 2017', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Pimpri-Chinchwad') & (data['Timestamp'].dt.year == 2017)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Pimpri-Chinchwad 2017', width=450, height=280)


</code>
"
2593,specific_pattern,Show a cumulative area chart of PM2.5 readings for Darbhanga across 2021.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Darbhanga') & (data['Timestamp'].dt.year == 2021)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Darbhanga 2021', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Darbhanga') & (data['Timestamp'].dt.year == 2021)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Darbhanga 2021', width=600, height=300)
     return chart


</code>
"
2594,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Pune, Rajamahendravaram, and Asansol in 2019 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Pune', 'Rajamahendravaram', 'Asansol']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Pune vs Rajamahendravaram vs Asansol – 2019', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Pune', 'Rajamahendravaram', 'Asansol']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Pune vs Rajamahendravaram vs Asansol – 2019', width=550, height=320)
     return chart


</code>
"
2595,temporal_aggregation,Show the monthly average PM2.5 for Hosur in 2023 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Hosur') & (data['Timestamp'].dt.year == 2023)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Hosur 2023', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Hosur') & (data['Timestamp'].dt.year == 2023)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Hosur 2023', width=450, height=280)


</code>
"
2596,specific_pattern,Plot the rolling 30-day average PM2.5 for Bihar in 2018 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Bihar') & (data['Timestamp'].dt.year == 2018)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Bihar 2018', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Bihar') & (data['Timestamp'].dt.year == 2018)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Bihar 2018', width=600, height=300)


</code>
"
2597,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Delhi, Jammu and Kashmir, and Meghalaya from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Delhi', 'Jammu and Kashmir', 'Meghalaya']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Delhi vs Jammu and Kashmir vs Meghalaya', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Delhi', 'Jammu and Kashmir', 'Meghalaya']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Delhi vs Jammu and Kashmir vs Meghalaya', width=550, height=320)
     return chart


</code>
"
2598,spatio_temporal_aggregation,"Visualize the monthly average PM10 for West Bengal, Rajasthan, and Himachal Pradesh in 2018 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['West Bengal', 'Rajasthan', 'Himachal Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2018)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: West Bengal, Rajasthan, UP – 2018', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['West Bengal', 'Rajasthan', 'Himachal Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2018)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: West Bengal, Rajasthan, UP – 2018', width=550, height=320)
     return chart


</code>
"
2599,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Gujarat, West Bengal, and Haryana from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Gujarat', 'West Bengal', 'Haryana']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Gujarat vs West Bengal vs Haryana', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Gujarat', 'West Bengal', 'Haryana']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Gujarat vs West Bengal vs Haryana', width=550, height=320)
     return chart


</code>
"
2600,temporal_aggregation,Show the monthly average PM2.5 for Arrah in 2024 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Arrah') & (data['Timestamp'].dt.year == 2024)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Arrah 2024', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Arrah') & (data['Timestamp'].dt.year == 2024)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Arrah 2024', width=450, height=280)


</code>
"
2601,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Tripura, Madhya Pradesh, and Haryana in 2017 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Tripura', 'Madhya Pradesh', 'Haryana']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2017)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Tripura, Madhya Pradesh, UP – 2017', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Tripura', 'Madhya Pradesh', 'Haryana']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2017)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Tripura, Madhya Pradesh, UP – 2017', width=550, height=320)
     return chart


</code>
"
2602,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Jalna, Gaya, and Vapi in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Jalna', 'Gaya', 'Vapi']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Jalna vs Gaya vs Vapi – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Jalna', 'Gaya', 'Vapi']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Jalna vs Gaya vs Vapi – 2023', width=550, height=320)
     return chart


</code>
"
2603,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Puducherry, Madhya Pradesh, and Sikkim from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Puducherry', 'Madhya Pradesh', 'Sikkim']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Puducherry vs Madhya Pradesh vs Sikkim', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Puducherry', 'Madhya Pradesh', 'Sikkim']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Puducherry vs Madhya Pradesh vs Sikkim', width=550, height=320)
     return chart


</code>
"
2604,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Bihar, Uttarakhand, and West Bengal from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Bihar', 'Uttarakhand', 'West Bengal']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Bihar vs Uttarakhand vs West Bengal', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Bihar', 'Uttarakhand', 'West Bengal']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Bihar vs Uttarakhand vs West Bengal', width=550, height=320)
     return chart


</code>
"
2605,specific_pattern,Plot the rolling 30-day average PM2.5 for Andhra Pradesh in 2017 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Andhra Pradesh') & (data['Timestamp'].dt.year == 2017)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Andhra Pradesh 2017', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Andhra Pradesh') & (data['Timestamp'].dt.year == 2017)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Andhra Pradesh 2017', width=600, height=300)


</code>
"
2606,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Haryana, Puducherry, and Tripura across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Haryana', 'Puducherry', 'Tripura']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Haryana', 'Puducherry', 'Tripura']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
2607,specific_pattern,Plot the rolling 30-day average PM2.5 for Arunachal Pradesh in 2020 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Arunachal Pradesh') & (data['Timestamp'].dt.year == 2020)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Arunachal Pradesh 2020', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Arunachal Pradesh') & (data['Timestamp'].dt.year == 2020)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Arunachal Pradesh 2020', width=600, height=300)


</code>
"
2608,temporal_aggregation,Show the monthly average PM2.5 for Dewas in 2023 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Dewas') & (data['Timestamp'].dt.year == 2023)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Dewas 2023', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Dewas') & (data['Timestamp'].dt.year == 2023)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Dewas 2023', width=450, height=280)


</code>
"
2609,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Uttar Pradesh, Chandigarh, and Madhya Pradesh from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Uttar Pradesh', 'Chandigarh', 'Madhya Pradesh']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Uttar Pradesh vs Chandigarh vs Madhya Pradesh', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Uttar Pradesh', 'Chandigarh', 'Madhya Pradesh']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Uttar Pradesh vs Chandigarh vs Madhya Pradesh', width=550, height=320)
     return chart


</code>
"
2610,temporal_aggregation,Show the monthly average PM2.5 for Samastipur in 2018 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Samastipur') & (data['Timestamp'].dt.year == 2018)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Samastipur 2018', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Samastipur') & (data['Timestamp'].dt.year == 2018)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Samastipur 2018', width=450, height=280)


</code>
"
2611,spatial_aggregation,"Show the top 10 states by average PM10 in 2020 as a bar chart, sorted in descending order.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2020]
    df = df.groupby('state')['PM10'].mean().reset_index().dropna()
    df = df.nlargest(10, 'PM10')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Top 10 States by Average PM10 in 2020', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2020]
     df = df.groupby('state')['PM10'].mean().reset_index().dropna()
     df = df.nlargest(10, 'PM10')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Top 10 States by Average PM10 in 2020', width=500, height=300)
     return chart


</code>
"
2612,temporal_aggregation,Show the monthly average PM2.5 for Prayagraj in 2017 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Prayagraj') & (data['Timestamp'].dt.year == 2017)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Prayagraj 2017', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Prayagraj') & (data['Timestamp'].dt.year == 2017)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Prayagraj 2017', width=450, height=280)


</code>
"
2613,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for West Bengal, Punjab, and Mizoram from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['West Bengal', 'Punjab', 'Mizoram']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: West Bengal vs Punjab vs Mizoram', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['West Bengal', 'Punjab', 'Mizoram']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: West Bengal vs Punjab vs Mizoram', width=550, height=320)
     return chart


</code>
"
2614,temporal_aggregation,Plot the weekly average PM2.5 for Pali in 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Pali') & (data['Timestamp'].dt.year == 2024)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Pali 2024', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Pali') & (data['Timestamp'].dt.year == 2024)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Pali 2024', width=600, height=300)
     return chart


</code>
"
2615,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Rajasthan, Chandigarh, and Jharkhand in 2019 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Rajasthan', 'Chandigarh', 'Jharkhand']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Rajasthan, Chandigarh, UP – 2019', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Rajasthan', 'Chandigarh', 'Jharkhand']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Rajasthan, Chandigarh, UP – 2019', width=550, height=320)
     return chart


</code>
"
2616,temporal_aggregation,Show a monthly bar chart of the number of days Odisha exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2018.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Odisha') & (data['Timestamp'].dt.year == 2018)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Odisha Exceeded WHO PM2.5 Guideline per Month – 2018', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Odisha') & (data['Timestamp'].dt.year == 2018)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Odisha Exceeded WHO PM2.5 Guideline per Month – 2018', width=500, height=300)
     return chart


</code>
"
2617,specific_pattern,Show a cumulative area chart of PM2.5 readings for Rajgir across 2024.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Rajgir') & (data['Timestamp'].dt.year == 2024)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Rajgir 2024', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Rajgir') & (data['Timestamp'].dt.year == 2024)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Rajgir 2024', width=600, height=300)
     return chart


</code>
"
2618,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Punjab, Delhi, and Nagaland from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Punjab', 'Delhi', 'Nagaland']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Punjab vs Delhi vs Nagaland', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Punjab', 'Delhi', 'Nagaland']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Punjab vs Delhi vs Nagaland', width=550, height=320)
     return chart


</code>
"
2619,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Chandigarh, Kerala, and Gujarat from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Chandigarh', 'Kerala', 'Gujarat']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Chandigarh vs Kerala vs Gujarat', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Chandigarh', 'Kerala', 'Gujarat']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Chandigarh vs Kerala vs Gujarat', width=550, height=320)
     return chart


</code>
"
2620,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Manipur, Arunachal Pradesh, and Jharkhand across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Manipur', 'Arunachal Pradesh', 'Jharkhand']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Manipur', 'Arunachal Pradesh', 'Jharkhand']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
2621,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Shillong, Palwal , and Jodhpur in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Shillong', 'Palwal ', 'Jodhpur']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Shillong vs Palwal  vs Jodhpur – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Shillong', 'Palwal ', 'Jodhpur']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Shillong vs Palwal  vs Jodhpur – 2022', width=550, height=320)
     return chart


</code>
"
2622,temporal_aggregation,Show the monthly average PM2.5 for Rajgir in 2019 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Rajgir') & (data['Timestamp'].dt.year == 2019)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Rajgir 2019', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Rajgir') & (data['Timestamp'].dt.year == 2019)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Rajgir 2019', width=450, height=280)


</code>
"
2623,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Yamuna Nagar, Visakhapatnam, and Mangalore in 2018 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Yamuna Nagar', 'Visakhapatnam', 'Mangalore']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2018)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Yamuna Nagar vs Visakhapatnam vs Mangalore – 2018', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Yamuna Nagar', 'Visakhapatnam', 'Mangalore']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2018)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Yamuna Nagar vs Visakhapatnam vs Mangalore – 2018', width=550, height=320)
     return chart


</code>
"
2624,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Sikkim, Chhattisgarh, and Karnataka from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Sikkim', 'Chhattisgarh', 'Karnataka']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Sikkim vs Chhattisgarh vs Karnataka', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Sikkim', 'Chhattisgarh', 'Karnataka']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Sikkim vs Chhattisgarh vs Karnataka', width=550, height=320)
     return chart


</code>
"
2625,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Bathinda, Dindigul, and Belgaum in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Bathinda', 'Dindigul', 'Belgaum']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Bathinda vs Dindigul vs Belgaum – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Bathinda', 'Dindigul', 'Belgaum']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Bathinda vs Dindigul vs Belgaum – 2022', width=550, height=320)
     return chart


</code>
"
2626,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Andhra Pradesh, Uttarakhand, and Mizoram across 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Andhra Pradesh', 'Uttarakhand', 'Mizoram']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Andhra Pradesh', 'Uttarakhand', 'Mizoram']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
     return chart


</code>
"
2627,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Puducherry, Assam, and Andhra Pradesh across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Puducherry', 'Assam', 'Andhra Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Puducherry', 'Assam', 'Andhra Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
2628,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Madhya Pradesh, Mizoram, and Meghalaya across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Madhya Pradesh', 'Mizoram', 'Meghalaya']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Madhya Pradesh', 'Mizoram', 'Meghalaya']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
2629,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Assam, Nagaland, and Tamil Nadu from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Assam', 'Nagaland', 'Tamil Nadu']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Assam vs Nagaland vs Tamil Nadu', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Assam', 'Nagaland', 'Tamil Nadu']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Assam vs Nagaland vs Tamil Nadu', width=550, height=320)
     return chart


</code>
"
2630,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Uttarakhand, Odisha, and Delhi in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Uttarakhand', 'Odisha', 'Delhi']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Uttarakhand, Odisha, UP – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Uttarakhand', 'Odisha', 'Delhi']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Uttarakhand, Odisha, UP – 2024', width=550, height=320)
     return chart


</code>
"
2631,specific_pattern,Show a cumulative area chart of PM2.5 readings for Greater Noida across 2019.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Greater Noida') & (data['Timestamp'].dt.year == 2019)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Greater Noida 2019', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Greater Noida') & (data['Timestamp'].dt.year == 2019)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Greater Noida 2019', width=600, height=300)
     return chart


</code>
"
2632,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Bihar, Arunachal Pradesh, and Chandigarh from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Bihar', 'Arunachal Pradesh', 'Chandigarh']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Bihar vs Arunachal Pradesh vs Chandigarh', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Bihar', 'Arunachal Pradesh', 'Chandigarh']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Bihar vs Arunachal Pradesh vs Chandigarh', width=550, height=320)
     return chart


</code>
"
2633,temporal_aggregation,Show the monthly average PM2.5 for Mangalore in 2020 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Mangalore') & (data['Timestamp'].dt.year == 2020)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Mangalore 2020', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Mangalore') & (data['Timestamp'].dt.year == 2020)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Mangalore 2020', width=450, height=280)


</code>
"
2634,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Haryana, Jammu and Kashmir, and Tripura across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Haryana', 'Jammu and Kashmir', 'Tripura']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Haryana', 'Jammu and Kashmir', 'Tripura']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
2635,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Punjab, Jammu and Kashmir, and Gujarat in 2019 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Punjab', 'Jammu and Kashmir', 'Gujarat']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Punjab, Jammu and Kashmir, UP – 2019', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Punjab', 'Jammu and Kashmir', 'Gujarat']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Punjab, Jammu and Kashmir, UP – 2019', width=550, height=320)
     return chart


</code>
"
2636,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for West Bengal, Assam, and Madhya Pradesh across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['West Bengal', 'Assam', 'Madhya Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['West Bengal', 'Assam', 'Madhya Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
2637,temporal_aggregation,Show the monthly average PM10 trend for Hisar from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Hisar'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Hisar (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Hisar'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Hisar (2019–2024)', width=600, height=300)
     return chart


</code>
"
2638,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Sikkim, Jammu and Kashmir, and Haryana in 2019 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Sikkim', 'Jammu and Kashmir', 'Haryana']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Sikkim, Jammu and Kashmir, UP – 2019', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Sikkim', 'Jammu and Kashmir', 'Haryana']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Sikkim, Jammu and Kashmir, UP – 2019', width=550, height=320)
     return chart


</code>
"
2639,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Himachal Pradesh, Haryana, and Punjab from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Himachal Pradesh', 'Haryana', 'Punjab']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Himachal Pradesh vs Haryana vs Punjab', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Himachal Pradesh', 'Haryana', 'Punjab']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Himachal Pradesh vs Haryana vs Punjab', width=550, height=320)
     return chart


</code>
"
2640,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Gujarat, Nagaland, and Telangana in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Gujarat', 'Nagaland', 'Telangana']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Gujarat, Nagaland, UP – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Gujarat', 'Nagaland', 'Telangana']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Gujarat, Nagaland, UP – 2024', width=550, height=320)
     return chart


</code>
"
2641,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Andhra Pradesh stations in 2019, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Andhra Pradesh') & (data['Timestamp'].dt.year == 2019)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Andhra Pradesh Stations 2019', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Andhra Pradesh') & (data['Timestamp'].dt.year == 2019)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Andhra Pradesh Stations 2019', width=450, height=350)


</code>
"
2642,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Arunachal Pradesh, Jammu and Kashmir, and Mizoram from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Arunachal Pradesh', 'Jammu and Kashmir', 'Mizoram']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Arunachal Pradesh vs Jammu and Kashmir vs Mizoram', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Arunachal Pradesh', 'Jammu and Kashmir', 'Mizoram']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Arunachal Pradesh vs Jammu and Kashmir vs Mizoram', width=550, height=320)
     return chart


</code>
"
2643,temporal_aggregation,Show the monthly average PM10 trend for Hapur from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Hapur'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Hapur (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Hapur'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Hapur (2019–2024)', width=600, height=300)
     return chart


</code>
"
2644,spatio_temporal_aggregation,"Visualize the monthly average PM10 for West Bengal, Bihar, and Andhra Pradesh in 2018 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['West Bengal', 'Bihar', 'Andhra Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2018)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: West Bengal, Bihar, UP – 2018', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['West Bengal', 'Bihar', 'Andhra Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2018)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: West Bengal, Bihar, UP – 2018', width=550, height=320)
     return chart


</code>
"
2645,temporal_aggregation,Show the monthly average PM10 trend for Aurangabad from 2017 to 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Aurangabad'].copy()
    df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Aurangabad (2017–2022)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Aurangabad'].copy()
     df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Aurangabad (2017–2022)', width=600, height=300)
     return chart


</code>
"
2646,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Himachal Pradesh, Arunachal Pradesh, and Haryana in 2017 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Himachal Pradesh', 'Arunachal Pradesh', 'Haryana']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2017)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Himachal Pradesh, Arunachal Pradesh, UP – 2017', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Himachal Pradesh', 'Arunachal Pradesh', 'Haryana']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2017)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Himachal Pradesh, Arunachal Pradesh, UP – 2017', width=550, height=320)
     return chart


</code>
"
2647,temporal_aggregation,Show the monthly average PM2.5 for Malegaon in 2019 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Malegaon') & (data['Timestamp'].dt.year == 2019)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Malegaon 2019', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Malegaon') & (data['Timestamp'].dt.year == 2019)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Malegaon 2019', width=450, height=280)


</code>
"
2648,temporal_aggregation,Show the monthly average PM2.5 for Ludhiana in 2019 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Ludhiana') & (data['Timestamp'].dt.year == 2019)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Ludhiana 2019', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Ludhiana') & (data['Timestamp'].dt.year == 2019)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Ludhiana 2019', width=450, height=280)


</code>
"
2649,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Assam, Uttarakhand, and Gujarat from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Assam', 'Uttarakhand', 'Gujarat']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Assam vs Uttarakhand vs Gujarat', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Assam', 'Uttarakhand', 'Gujarat']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Assam vs Uttarakhand vs Gujarat', width=550, height=320)
     return chart


</code>
"
2650,temporal_aggregation,Show the monthly average PM2.5 for Panipat in 2024 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Panipat') & (data['Timestamp'].dt.year == 2024)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Panipat 2024', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Panipat') & (data['Timestamp'].dt.year == 2024)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Panipat 2024', width=450, height=280)


</code>
"
2651,temporal_aggregation,Show the monthly average PM2.5 for Rourkela in 2019 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Rourkela') & (data['Timestamp'].dt.year == 2019)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Rourkela 2019', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Rourkela') & (data['Timestamp'].dt.year == 2019)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Rourkela 2019', width=450, height=280)


</code>
"
2652,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Ambala, Brajrajnagar, and Guwahati in 2020 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Ambala', 'Brajrajnagar', 'Guwahati']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2020)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Ambala vs Brajrajnagar vs Guwahati – 2020', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Ambala', 'Brajrajnagar', 'Guwahati']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2020)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Ambala vs Brajrajnagar vs Guwahati – 2020', width=550, height=320)
     return chart


</code>
"
2653,temporal_aggregation,Show the monthly average PM2.5 for Kolar in 2017 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Kolar') & (data['Timestamp'].dt.year == 2017)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Kolar 2017', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Kolar') & (data['Timestamp'].dt.year == 2017)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Kolar 2017', width=450, height=280)


</code>
"
2654,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Meghalaya, Telangana, and Tamil Nadu from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Meghalaya', 'Telangana', 'Tamil Nadu']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Meghalaya vs Telangana vs Tamil Nadu', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Meghalaya', 'Telangana', 'Tamil Nadu']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Meghalaya vs Telangana vs Tamil Nadu', width=550, height=320)
     return chart


</code>
"
2655,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Jammu and Kashmir, Madhya Pradesh, and Chhattisgarh across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Jammu and Kashmir', 'Madhya Pradesh', 'Chhattisgarh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Jammu and Kashmir', 'Madhya Pradesh', 'Chhattisgarh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
2656,temporal_aggregation,Show a monthly bar chart of the number of days Delhi exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2018.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Delhi') & (data['Timestamp'].dt.year == 2018)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Delhi Exceeded WHO PM2.5 Guideline per Month – 2018', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Delhi') & (data['Timestamp'].dt.year == 2018)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Delhi Exceeded WHO PM2.5 Guideline per Month – 2018', width=500, height=300)
     return chart


</code>
"
2657,temporal_aggregation,Show the monthly average PM10 trend for Imphal from 2017 to 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Imphal'].copy()
    df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Imphal (2017–2022)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Imphal'].copy()
     df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Imphal (2017–2022)', width=600, height=300)
     return chart


</code>
"
2658,temporal_aggregation,Show the monthly average PM10 trend for Palwal from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Palwal'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Palwal (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Palwal'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Palwal (2019–2024)', width=600, height=300)
     return chart


</code>
"
2659,temporal_aggregation,Plot the weekly average PM2.5 for Ajmer in 2018 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Ajmer') & (data['Timestamp'].dt.year == 2018)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Ajmer 2018', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Ajmer') & (data['Timestamp'].dt.year == 2018)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Ajmer 2018', width=600, height=300)
     return chart


</code>
"
2660,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Andhra Pradesh, Delhi, and Gujarat from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Andhra Pradesh', 'Delhi', 'Gujarat']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Andhra Pradesh vs Delhi vs Gujarat', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Andhra Pradesh', 'Delhi', 'Gujarat']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Andhra Pradesh vs Delhi vs Gujarat', width=550, height=320)
     return chart


</code>
"
2661,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Bathinda, Rajamahendravaram, and Samastipur in 2020 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Bathinda', 'Rajamahendravaram', 'Samastipur']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2020)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Bathinda vs Rajamahendravaram vs Samastipur – 2020', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Bathinda', 'Rajamahendravaram', 'Samastipur']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2020)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Bathinda vs Rajamahendravaram vs Samastipur – 2020', width=550, height=320)
     return chart


</code>
"
2662,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Rajasthan, Meghalaya, and Mizoram from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Rajasthan', 'Meghalaya', 'Mizoram']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Rajasthan vs Meghalaya vs Mizoram', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Rajasthan', 'Meghalaya', 'Mizoram']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Rajasthan vs Meghalaya vs Mizoram', width=550, height=320)
     return chart


</code>
"
2663,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Indore, Dholpur, and Singrauli in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Indore', 'Dholpur', 'Singrauli']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Indore vs Dholpur vs Singrauli – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Indore', 'Dholpur', 'Singrauli']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Indore vs Dholpur vs Singrauli – 2024', width=550, height=320)
     return chart


</code>
"
2664,temporal_aggregation,Show the monthly average PM10 trend for Chengalpattu from 2017 to 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Chengalpattu'].copy()
    df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Chengalpattu (2017–2022)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Chengalpattu'].copy()
     df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Chengalpattu (2017–2022)', width=600, height=300)
     return chart


</code>
"
2665,temporal_aggregation,Show the monthly average PM10 trend for Ballabgarh from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Ballabgarh'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Ballabgarh (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Ballabgarh'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Ballabgarh (2019–2024)', width=600, height=300)
     return chart


</code>
"
2666,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Haryana stations in 2024, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Haryana') & (data['Timestamp'].dt.year == 2024)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Haryana Stations 2024', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Haryana') & (data['Timestamp'].dt.year == 2024)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Haryana Stations 2024', width=450, height=350)


</code>
"
2667,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Kaithal, Ernakulam, and Boisar in 2020 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Kaithal', 'Ernakulam', 'Boisar']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2020)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Kaithal vs Ernakulam vs Boisar – 2020', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Kaithal', 'Ernakulam', 'Boisar']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2020)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Kaithal vs Ernakulam vs Boisar – 2020', width=550, height=320)
     return chart


</code>
"
2668,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Meghalaya, Uttarakhand, and Tripura from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Meghalaya', 'Uttarakhand', 'Tripura']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Meghalaya vs Uttarakhand vs Tripura', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Meghalaya', 'Uttarakhand', 'Tripura']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Meghalaya vs Uttarakhand vs Tripura', width=550, height=320)
     return chart


</code>
"
2669,specific_pattern,Show a cumulative area chart of PM2.5 readings for Narnaul across 2019.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Narnaul') & (data['Timestamp'].dt.year == 2019)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Narnaul 2019', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Narnaul') & (data['Timestamp'].dt.year == 2019)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Narnaul 2019', width=600, height=300)
     return chart


</code>
"
2670,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Odisha, Gujarat, and Delhi in 2021 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Odisha', 'Gujarat', 'Delhi']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Odisha, Gujarat, UP – 2021', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Odisha', 'Gujarat', 'Delhi']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Odisha, Gujarat, UP – 2021', width=550, height=320)
     return chart


</code>
"
2671,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Punjab, Jharkhand, and Meghalaya in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Punjab', 'Jharkhand', 'Meghalaya']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Punjab, Jharkhand, UP – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Punjab', 'Jharkhand', 'Meghalaya']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Punjab, Jharkhand, UP – 2023', width=550, height=320)
     return chart


</code>
"
2672,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Uttarakhand stations in 2024, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Uttarakhand') & (data['Timestamp'].dt.year == 2024)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Uttarakhand Stations 2024', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Uttarakhand') & (data['Timestamp'].dt.year == 2024)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Uttarakhand Stations 2024', width=450, height=350)


</code>
"
2673,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Kunjemura, Durgapur, and Mira-Bhayandar in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Kunjemura', 'Durgapur', 'Mira-Bhayandar']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Kunjemura vs Durgapur vs Mira-Bhayandar – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Kunjemura', 'Durgapur', 'Mira-Bhayandar']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Kunjemura vs Durgapur vs Mira-Bhayandar – 2023', width=550, height=320)
     return chart


</code>
"
2674,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Gangtok, Gandhinagar, and Nanded in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Gangtok', 'Gandhinagar', 'Nanded']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Gangtok vs Gandhinagar vs Nanded – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Gangtok', 'Gandhinagar', 'Nanded']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Gangtok vs Gandhinagar vs Nanded – 2022', width=550, height=320)
     return chart


</code>
"
2675,temporal_aggregation,Show the monthly average PM2.5 for Ramanathapuram in 2024 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Ramanathapuram') & (data['Timestamp'].dt.year == 2024)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Ramanathapuram 2024', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Ramanathapuram') & (data['Timestamp'].dt.year == 2024)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Ramanathapuram 2024', width=450, height=280)


</code>
"
2676,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Gujarat, Madhya Pradesh, and Gujarat from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Gujarat', 'Madhya Pradesh', 'Gujarat']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Gujarat vs Madhya Pradesh vs Gujarat', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Gujarat', 'Madhya Pradesh', 'Gujarat']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Gujarat vs Madhya Pradesh vs Gujarat', width=550, height=320)
     return chart


</code>
"
2677,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Vijayapura, Angul, and Thanjavur in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Vijayapura', 'Angul', 'Thanjavur']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Vijayapura vs Angul vs Thanjavur – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Vijayapura', 'Angul', 'Thanjavur']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Vijayapura vs Angul vs Thanjavur – 2022', width=550, height=320)
     return chart


</code>
"
2678,temporal_aggregation,Show the monthly average PM2.5 for Boisar in 2018 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Boisar') & (data['Timestamp'].dt.year == 2018)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Boisar 2018', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Boisar') & (data['Timestamp'].dt.year == 2018)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Boisar 2018', width=450, height=280)


</code>
"
2679,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Odisha, Sikkim, and Meghalaya from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Odisha', 'Sikkim', 'Meghalaya']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Odisha vs Sikkim vs Meghalaya', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Odisha', 'Sikkim', 'Meghalaya']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Odisha vs Sikkim vs Meghalaya', width=550, height=320)
     return chart


</code>
"
2680,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Punjab, Chhattisgarh, and Tripura from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Punjab', 'Chhattisgarh', 'Tripura']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Punjab vs Chhattisgarh vs Tripura', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Punjab', 'Chhattisgarh', 'Tripura']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Punjab vs Chhattisgarh vs Tripura', width=550, height=320)
     return chart


</code>
"
2681,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Meghalaya, Karnataka, and Karnataka across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Meghalaya', 'Karnataka', 'Karnataka']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Meghalaya', 'Karnataka', 'Karnataka']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
2682,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Navi Mumbai, Barbil, and Udupi in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Navi Mumbai', 'Barbil', 'Udupi']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Navi Mumbai vs Barbil vs Udupi – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Navi Mumbai', 'Barbil', 'Udupi']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Navi Mumbai vs Barbil vs Udupi – 2022', width=550, height=320)
     return chart


</code>
"
2683,specific_pattern,Plot the rolling 30-day average PM2.5 for Arunachal Pradesh in 2017 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Arunachal Pradesh') & (data['Timestamp'].dt.year == 2017)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Arunachal Pradesh 2017', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Arunachal Pradesh') & (data['Timestamp'].dt.year == 2017)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Arunachal Pradesh 2017', width=600, height=300)


</code>
"
2684,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Delhi, Puducherry, and Himachal Pradesh across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Delhi', 'Puducherry', 'Himachal Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Delhi', 'Puducherry', 'Himachal Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
2685,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Chhattisgarh, Delhi, and Assam across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Chhattisgarh', 'Delhi', 'Assam']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Chhattisgarh', 'Delhi', 'Assam']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
2686,specific_pattern,Show a cumulative area chart of PM2.5 readings for Bidar across 2022.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Bidar') & (data['Timestamp'].dt.year == 2022)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Bidar 2022', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Bidar') & (data['Timestamp'].dt.year == 2022)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Bidar 2022', width=600, height=300)
     return chart


</code>
"
2687,temporal_aggregation,Show a monthly bar chart of the number of days Punjab exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2022.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Punjab') & (data['Timestamp'].dt.year == 2022)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Punjab Exceeded WHO PM2.5 Guideline per Month – 2022', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Punjab') & (data['Timestamp'].dt.year == 2022)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Punjab Exceeded WHO PM2.5 Guideline per Month – 2022', width=500, height=300)
     return chart


</code>
"
2688,temporal_aggregation,Show the monthly average PM2.5 for Vellore in 2017 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Vellore') & (data['Timestamp'].dt.year == 2017)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Vellore 2017', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Vellore') & (data['Timestamp'].dt.year == 2017)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Vellore 2017', width=450, height=280)


</code>
"
2689,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Kerala, Rajasthan, and Andhra Pradesh across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Kerala', 'Rajasthan', 'Andhra Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Kerala', 'Rajasthan', 'Andhra Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
2690,specific_pattern,Show a cumulative area chart of PM2.5 readings for Dharwad across 2023.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Dharwad') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Dharwad 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Dharwad') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Dharwad 2023', width=600, height=300)
     return chart


</code>
"
2691,specific_pattern,Show a cumulative area chart of PM2.5 readings for Dewas across 2021.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Dewas') & (data['Timestamp'].dt.year == 2021)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Dewas 2021', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Dewas') & (data['Timestamp'].dt.year == 2021)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Dewas 2021', width=600, height=300)
     return chart


</code>
"
2692,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Chhattisgarh, Gujarat, and Tripura in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Chhattisgarh', 'Gujarat', 'Tripura']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Chhattisgarh, Gujarat, UP – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Chhattisgarh', 'Gujarat', 'Tripura']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Chhattisgarh, Gujarat, UP – 2024', width=550, height=320)
     return chart


</code>
"
2693,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for West Bengal, Bihar, and Sikkim across 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['West Bengal', 'Bihar', 'Sikkim']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['West Bengal', 'Bihar', 'Sikkim']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
     return chart


</code>
"
2694,temporal_aggregation,Show a monthly bar chart of the number of days Uttar Pradesh exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2018.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Uttar Pradesh') & (data['Timestamp'].dt.year == 2018)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Uttar Pradesh Exceeded WHO PM2.5 Guideline per Month – 2018', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Uttar Pradesh') & (data['Timestamp'].dt.year == 2018)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Uttar Pradesh Exceeded WHO PM2.5 Guideline per Month – 2018', width=500, height=300)
     return chart


</code>
"
2695,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Punjab, Chandigarh, and Nagaland from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Punjab', 'Chandigarh', 'Nagaland']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Punjab vs Chandigarh vs Nagaland', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Punjab', 'Chandigarh', 'Nagaland']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Punjab vs Chandigarh vs Nagaland', width=550, height=320)
     return chart


</code>
"
2696,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Jharkhand, Chhattisgarh, and Uttar Pradesh from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Jharkhand', 'Chhattisgarh', 'Uttar Pradesh']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Jharkhand vs Chhattisgarh vs Uttar Pradesh', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Jharkhand', 'Chhattisgarh', 'Uttar Pradesh']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Jharkhand vs Chhattisgarh vs Uttar Pradesh', width=550, height=320)
     return chart


</code>
"
2697,spatial_aggregation,Show a bar chart of the top 12 cities by median PM2.5 in 2021.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2021]
    df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
    df = df.nlargest(12, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
        y=alt.Y('city:N', sort='-x', title='City'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
        tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 12 Cities by Median PM2.5 in 2021', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2021]
     df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
     df = df.nlargest(12, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
         y=alt.Y('city:N', sort='-x', title='City'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
         tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 12 Cities by Median PM2.5 in 2021', width=500, height=300)
     return chart


</code>
"
2698,specific_pattern,Show a cumulative area chart of PM2.5 readings for Panchkula across 2017.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Panchkula') & (data['Timestamp'].dt.year == 2017)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Panchkula 2017', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Panchkula') & (data['Timestamp'].dt.year == 2017)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Panchkula 2017', width=600, height=300)
     return chart


</code>
"
2699,specific_pattern,Show a cumulative area chart of PM2.5 readings for Nagpur across 2021.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Nagpur') & (data['Timestamp'].dt.year == 2021)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Nagpur 2021', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Nagpur') & (data['Timestamp'].dt.year == 2021)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Nagpur 2021', width=600, height=300)
     return chart


</code>
"
2700,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Nagaland, Puducherry, and Haryana from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Nagaland', 'Puducherry', 'Haryana']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Nagaland vs Puducherry vs Haryana', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Nagaland', 'Puducherry', 'Haryana']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Nagaland vs Puducherry vs Haryana', width=550, height=320)
     return chart


</code>
"
2701,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Sikkim, Rajasthan, and Madhya Pradesh across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Sikkim', 'Rajasthan', 'Madhya Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Sikkim', 'Rajasthan', 'Madhya Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
2702,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Assam, Madhya Pradesh, and Sikkim across 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Assam', 'Madhya Pradesh', 'Sikkim']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Assam', 'Madhya Pradesh', 'Sikkim']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
     return chart


</code>
"
2703,temporal_aggregation,Plot the weekly average PM2.5 for Udaipur in 2020 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Udaipur') & (data['Timestamp'].dt.year == 2020)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Udaipur 2020', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Udaipur') & (data['Timestamp'].dt.year == 2020)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Udaipur 2020', width=600, height=300)
     return chart


</code>
"
2704,temporal_aggregation,Show the monthly average PM2.5 for Badlapur in 2024 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Badlapur') & (data['Timestamp'].dt.year == 2024)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Badlapur 2024', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Badlapur') & (data['Timestamp'].dt.year == 2024)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Badlapur 2024', width=450, height=280)


</code>
"
2705,specific_pattern,Plot the rolling 30-day average PM2.5 for Rajasthan in 2019 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Rajasthan') & (data['Timestamp'].dt.year == 2019)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Rajasthan 2019', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Rajasthan') & (data['Timestamp'].dt.year == 2019)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Rajasthan 2019', width=600, height=300)


</code>
"
2706,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Rairangpur, Gandhinagar, and Yamuna Nagar in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Rairangpur', 'Gandhinagar', 'Yamuna Nagar']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Rairangpur vs Gandhinagar vs Yamuna Nagar – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Rairangpur', 'Gandhinagar', 'Yamuna Nagar']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Rairangpur vs Gandhinagar vs Yamuna Nagar – 2024', width=550, height=320)
     return chart


</code>
"
2707,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Mizoram, Manipur, and Arunachal Pradesh across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Mizoram', 'Manipur', 'Arunachal Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Mizoram', 'Manipur', 'Arunachal Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
2708,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Haryana, Himachal Pradesh, and Uttarakhand from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Haryana', 'Himachal Pradesh', 'Uttarakhand']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Haryana vs Himachal Pradesh vs Uttarakhand', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Haryana', 'Himachal Pradesh', 'Uttarakhand']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Haryana vs Himachal Pradesh vs Uttarakhand', width=550, height=320)
     return chart


</code>
"
2709,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Chandigarh, Uttarakhand, and Jammu and Kashmir across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Chandigarh', 'Uttarakhand', 'Jammu and Kashmir']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Chandigarh', 'Uttarakhand', 'Jammu and Kashmir']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
2710,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Chhattisgarh, Tripura, and West Bengal across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Chhattisgarh', 'Tripura', 'West Bengal']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Chhattisgarh', 'Tripura', 'West Bengal']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
2711,temporal_aggregation,Plot the weekly average PM2.5 for Khurja in 2023 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Khurja') & (data['Timestamp'].dt.year == 2023)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Khurja 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Khurja') & (data['Timestamp'].dt.year == 2023)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Khurja 2023', width=600, height=300)
     return chart


</code>
"
2712,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Navi Mumbai, Shivamogga, and Kollam in 2020 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Navi Mumbai', 'Shivamogga', 'Kollam']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2020)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Navi Mumbai vs Shivamogga vs Kollam – 2020', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Navi Mumbai', 'Shivamogga', 'Kollam']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2020)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Navi Mumbai vs Shivamogga vs Kollam – 2020', width=550, height=320)
     return chart


</code>
"
2713,temporal_aggregation,Show the monthly average PM10 trend for Mahad from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Mahad'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Mahad (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Mahad'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Mahad (2019–2024)', width=600, height=300)
     return chart


</code>
"
2714,specific_pattern,Show a cumulative area chart of PM2.5 readings for Baran across 2024.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Baran') & (data['Timestamp'].dt.year == 2024)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Baran 2024', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Baran') & (data['Timestamp'].dt.year == 2024)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Baran 2024', width=600, height=300)
     return chart


</code>
"
2715,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Chandigarh, Odisha, and Madhya Pradesh in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Chandigarh', 'Odisha', 'Madhya Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Chandigarh, Odisha, UP – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Chandigarh', 'Odisha', 'Madhya Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Chandigarh, Odisha, UP – 2024', width=550, height=320)
     return chart


</code>
"
2716,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Meghalaya, Rajasthan, and Odisha across 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Meghalaya', 'Rajasthan', 'Odisha']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Meghalaya', 'Rajasthan', 'Odisha']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
     return chart


</code>
"
2717,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Meghalaya, Jharkhand, and Jammu and Kashmir in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Meghalaya', 'Jharkhand', 'Jammu and Kashmir']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Meghalaya, Jharkhand, UP – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Meghalaya', 'Jharkhand', 'Jammu and Kashmir']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Meghalaya, Jharkhand, UP – 2022', width=550, height=320)
     return chart


</code>
"
2718,spatio_temporal_aggregation,Show a heatmap of average PM2.5 for the top 14 most polluted states by month for 2019.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    top8 = data.groupby('state')['PM2.5'].mean().nlargest(14).index.tolist()
    df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_rect().encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('state:N', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
        tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Heatmap – Top 14 Polluted States by Month (2019)', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     top8 = data.groupby('state')['PM2.5'].mean().nlargest(14).index.tolist()
     df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_rect().encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('state:N', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
         tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Heatmap – Top 14 Polluted States by Month (2019)', width=500, height=300)
     return chart


</code>
"
2719,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Sikkim, Odisha, and Jharkhand from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Sikkim', 'Odisha', 'Jharkhand']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Sikkim vs Odisha vs Jharkhand', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Sikkim', 'Odisha', 'Jharkhand']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Sikkim vs Odisha vs Jharkhand', width=550, height=320)
     return chart


</code>
"
2720,specific_pattern,Show a cumulative area chart of PM2.5 readings for Rupnagar across 2024.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Rupnagar') & (data['Timestamp'].dt.year == 2024)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Rupnagar 2024', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Rupnagar') & (data['Timestamp'].dt.year == 2024)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Rupnagar 2024', width=600, height=300)
     return chart


</code>
"
2721,temporal_aggregation,Show the monthly average PM2.5 for Keonjhar in 2019 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Keonjhar') & (data['Timestamp'].dt.year == 2019)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Keonjhar 2019', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Keonjhar') & (data['Timestamp'].dt.year == 2019)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Keonjhar 2019', width=450, height=280)


</code>
"
2722,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Manipur, Odisha, and Uttarakhand in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Manipur', 'Odisha', 'Uttarakhand']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Manipur, Odisha, UP – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Manipur', 'Odisha', 'Uttarakhand']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Manipur, Odisha, UP – 2024', width=550, height=320)
     return chart


</code>
"
2723,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Manipur, Meghalaya, and Meghalaya across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Manipur', 'Meghalaya', 'Meghalaya']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Manipur', 'Meghalaya', 'Meghalaya']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
2724,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Jammu and Kashmir, Assam, and Puducherry across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Jammu and Kashmir', 'Assam', 'Puducherry']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Jammu and Kashmir', 'Assam', 'Puducherry']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
2725,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Manipur, Manipur, and Tamil Nadu from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Manipur', 'Manipur', 'Tamil Nadu']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Manipur vs Manipur vs Tamil Nadu', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Manipur', 'Manipur', 'Tamil Nadu']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Manipur vs Manipur vs Tamil Nadu', width=550, height=320)
     return chart


</code>
"
2726,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Delhi, Haryana, and Himachal Pradesh across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Delhi', 'Haryana', 'Himachal Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Delhi', 'Haryana', 'Himachal Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
2727,temporal_aggregation,Show the monthly average PM10 trend for Nagpur from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Nagpur'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Nagpur (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Nagpur'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Nagpur (2019–2024)', width=600, height=300)
     return chart


</code>
"
2728,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Uttar Pradesh, Arunachal Pradesh, and Jammu and Kashmir across 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Uttar Pradesh', 'Arunachal Pradesh', 'Jammu and Kashmir']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Uttar Pradesh', 'Arunachal Pradesh', 'Jammu and Kashmir']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
     return chart


</code>
"
2729,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Mizoram, Meghalaya, and Andhra Pradesh from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Mizoram', 'Meghalaya', 'Andhra Pradesh']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Mizoram vs Meghalaya vs Andhra Pradesh', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Mizoram', 'Meghalaya', 'Andhra Pradesh']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Mizoram vs Meghalaya vs Andhra Pradesh', width=550, height=320)
     return chart


</code>
"
2730,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Telangana, Puducherry, and Uttarakhand from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Telangana', 'Puducherry', 'Uttarakhand']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Telangana vs Puducherry vs Uttarakhand', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Telangana', 'Puducherry', 'Uttarakhand']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Telangana vs Puducherry vs Uttarakhand', width=550, height=320)
     return chart


</code>
"
2731,spatial_aggregation,Show a bar chart of the top 7 cities by median PM2.5 in 2024.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2024]
    df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
    df = df.nlargest(7, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
        y=alt.Y('city:N', sort='-x', title='City'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
        tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 7 Cities by Median PM2.5 in 2024', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2024]
     df = df.groupby('city')['PM2.5'].median().reset_index().dropna()
     df = df.nlargest(7, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Median PM2.5 (µg/m³)'),
         y=alt.Y('city:N', sort='-x', title='City'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='inferno'), legend=None),
         tooltip=['city:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 7 Cities by Median PM2.5 in 2024', width=500, height=300)
     return chart


</code>
"
2732,temporal_aggregation,Plot the weekly average PM2.5 for Solapur in 2020 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Solapur') & (data['Timestamp'].dt.year == 2020)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Solapur 2020', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Solapur') & (data['Timestamp'].dt.year == 2020)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Solapur 2020', width=600, height=300)
     return chart


</code>
"
2733,temporal_aggregation,Plot the weekly average PM2.5 for Araria in 2023 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Araria') & (data['Timestamp'].dt.year == 2023)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Araria 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Araria') & (data['Timestamp'].dt.year == 2023)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Araria 2023', width=600, height=300)
     return chart


</code>
"
2734,temporal_aggregation,Plot the weekly average PM2.5 for Srinagar in 2021 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Srinagar') & (data['Timestamp'].dt.year == 2021)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Srinagar 2021', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Srinagar') & (data['Timestamp'].dt.year == 2021)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Srinagar 2021', width=600, height=300)
     return chart


</code>
"
2735,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Sikkim, Mizoram, and Assam across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Sikkim', 'Mizoram', 'Assam']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Sikkim', 'Mizoram', 'Assam']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
2736,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Telangana, Maharashtra, and Meghalaya in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Telangana', 'Maharashtra', 'Meghalaya']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Telangana, Maharashtra, UP – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Telangana', 'Maharashtra', 'Meghalaya']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Telangana, Maharashtra, UP – 2022', width=550, height=320)
     return chart


</code>
"
2737,temporal_aggregation,Show a monthly bar chart of the number of days Bihar exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2024.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Bihar') & (data['Timestamp'].dt.year == 2024)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Bihar Exceeded WHO PM2.5 Guideline per Month – 2024', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Bihar') & (data['Timestamp'].dt.year == 2024)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Bihar Exceeded WHO PM2.5 Guideline per Month – 2024', width=500, height=300)
     return chart


</code>
"
2738,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Mizoram, Gujarat, and Delhi across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Mizoram', 'Gujarat', 'Delhi']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Mizoram', 'Gujarat', 'Delhi']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
2739,temporal_aggregation,Show the monthly average PM10 trend for Amravati from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Amravati'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Amravati (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Amravati'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Amravati (2019–2024)', width=600, height=300)
     return chart


</code>
"
2740,temporal_aggregation,Show the monthly average PM10 trend for Kollam from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Kollam'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Kollam (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Kollam'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Kollam (2019–2024)', width=600, height=300)
     return chart


</code>
"
2741,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Mizoram, Punjab, and Arunachal Pradesh across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Mizoram', 'Punjab', 'Arunachal Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Mizoram', 'Punjab', 'Arunachal Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
2742,temporal_aggregation,Plot the weekly average PM2.5 for Palwal in 2020 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Palwal') & (data['Timestamp'].dt.year == 2020)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Palwal 2020', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Palwal') & (data['Timestamp'].dt.year == 2020)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Palwal 2020', width=600, height=300)
     return chart


</code>
"
2743,temporal_aggregation,Show the monthly average PM10 trend for Bhagalpur from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Bhagalpur'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Bhagalpur (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Bhagalpur'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Bhagalpur (2019–2024)', width=600, height=300)
     return chart


</code>
"
2744,temporal_aggregation,Plot the weekly average PM2.5 for Manguraha in 2023 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Manguraha') & (data['Timestamp'].dt.year == 2023)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Manguraha 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Manguraha') & (data['Timestamp'].dt.year == 2023)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Manguraha 2023', width=600, height=300)
     return chart


</code>
"
2745,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Puducherry, West Bengal, and Nagaland in 2021 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Puducherry', 'West Bengal', 'Nagaland']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Puducherry, West Bengal, UP – 2021', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Puducherry', 'West Bengal', 'Nagaland']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Puducherry, West Bengal, UP – 2021', width=550, height=320)
     return chart


</code>
"
2746,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Bettiah, Patiala, and Begusarai in 2019 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Bettiah', 'Patiala', 'Begusarai']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Bettiah vs Patiala vs Begusarai – 2019', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Bettiah', 'Patiala', 'Begusarai']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Bettiah vs Patiala vs Begusarai – 2019', width=550, height=320)
     return chart


</code>
"
2747,specific_pattern,Show a cumulative area chart of PM2.5 readings for Agra across 2024.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Agra') & (data['Timestamp'].dt.year == 2024)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Agra 2024', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Agra') & (data['Timestamp'].dt.year == 2024)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Agra 2024', width=600, height=300)
     return chart


</code>
"
2748,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Sikkim, Telangana, and Jammu and Kashmir in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Sikkim', 'Telangana', 'Jammu and Kashmir']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Sikkim, Telangana, UP – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Sikkim', 'Telangana', 'Jammu and Kashmir']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Sikkim, Telangana, UP – 2024', width=550, height=320)
     return chart


</code>
"
2749,temporal_aggregation,Show the monthly average PM2.5 for Guwahati in 2019 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Guwahati') & (data['Timestamp'].dt.year == 2019)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Guwahati 2019', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Guwahati') & (data['Timestamp'].dt.year == 2019)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Guwahati 2019', width=450, height=280)


</code>
"
2750,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Delhi, Himachal Pradesh, and Karnataka from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Delhi', 'Himachal Pradesh', 'Karnataka']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Delhi vs Himachal Pradesh vs Karnataka', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Delhi', 'Himachal Pradesh', 'Karnataka']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Delhi vs Himachal Pradesh vs Karnataka', width=550, height=320)
     return chart


</code>
"
2751,temporal_aggregation,Show the monthly average PM2.5 for Pimpri-Chinchwad in 2022 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Pimpri-Chinchwad') & (data['Timestamp'].dt.year == 2022)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Pimpri-Chinchwad 2022', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Pimpri-Chinchwad') & (data['Timestamp'].dt.year == 2022)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Pimpri-Chinchwad 2022', width=450, height=280)


</code>
"
2752,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Kadapa, Baddi, and Dharuhera in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Kadapa', 'Baddi', 'Dharuhera']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Kadapa vs Baddi vs Dharuhera – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Kadapa', 'Baddi', 'Dharuhera']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Kadapa vs Baddi vs Dharuhera – 2024', width=550, height=320)
     return chart


</code>
"
2753,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Uttar Pradesh, Nagaland, and Assam from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Uttar Pradesh', 'Nagaland', 'Assam']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Uttar Pradesh vs Nagaland vs Assam', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Uttar Pradesh', 'Nagaland', 'Assam']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Uttar Pradesh vs Nagaland vs Assam', width=550, height=320)
     return chart


</code>
"
2754,temporal_aggregation,Show the monthly average PM10 trend for Kalyan from 2017 to 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Kalyan'].copy()
    df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Kalyan (2017–2022)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Kalyan'].copy()
     df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Kalyan (2017–2022)', width=600, height=300)
     return chart


</code>
"
2755,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Pimpri-Chinchwad, Dhule, and Haldia in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Pimpri-Chinchwad', 'Dhule', 'Haldia']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Pimpri-Chinchwad vs Dhule vs Haldia – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Pimpri-Chinchwad', 'Dhule', 'Haldia']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Pimpri-Chinchwad vs Dhule vs Haldia – 2024', width=550, height=320)
     return chart


</code>
"
2756,temporal_aggregation,Show the monthly average PM2.5 for Nanded in 2019 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Nanded') & (data['Timestamp'].dt.year == 2019)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Nanded 2019', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Nanded') & (data['Timestamp'].dt.year == 2019)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Nanded 2019', width=450, height=280)


</code>
"
2757,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Tamil Nadu, Kerala, and Kerala in 2021 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Tamil Nadu', 'Kerala', 'Kerala']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Tamil Nadu, Kerala, UP – 2021', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Tamil Nadu', 'Kerala', 'Kerala']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Tamil Nadu, Kerala, UP – 2021', width=550, height=320)
     return chart


</code>
"
2758,temporal_aggregation,Show the monthly average PM10 trend for Mumbai from 2017 to 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Mumbai'].copy()
    df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Mumbai (2017–2022)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Mumbai'].copy()
     df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Mumbai (2017–2022)', width=600, height=300)
     return chart


</code>
"
2759,temporal_aggregation,Show the monthly average PM2.5 for Vapi in 2020 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Vapi') & (data['Timestamp'].dt.year == 2020)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Vapi 2020', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Vapi') & (data['Timestamp'].dt.year == 2020)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Vapi 2020', width=450, height=280)


</code>
"
2760,temporal_aggregation,Plot the weekly average PM2.5 for Sagar in 2021 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Sagar') & (data['Timestamp'].dt.year == 2021)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Sagar 2021', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Sagar') & (data['Timestamp'].dt.year == 2021)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Sagar 2021', width=600, height=300)
     return chart


</code>
"
2761,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Bundi, Panipat, and Karur in 2019 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Bundi', 'Panipat', 'Karur']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Bundi vs Panipat vs Karur – 2019', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Bundi', 'Panipat', 'Karur']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Bundi vs Panipat vs Karur – 2019', width=550, height=320)
     return chart


</code>
"
2762,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Sikkim, Tripura, and Himachal Pradesh from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Sikkim', 'Tripura', 'Himachal Pradesh']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Sikkim vs Tripura vs Himachal Pradesh', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Sikkim', 'Tripura', 'Himachal Pradesh']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Sikkim vs Tripura vs Himachal Pradesh', width=550, height=320)
     return chart


</code>
"
2763,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Uttarakhand, Maharashtra, and Chandigarh from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Uttarakhand', 'Maharashtra', 'Chandigarh']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Uttarakhand vs Maharashtra vs Chandigarh', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Uttarakhand', 'Maharashtra', 'Chandigarh']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Uttarakhand vs Maharashtra vs Chandigarh', width=550, height=320)
     return chart


</code>
"
2764,temporal_aggregation,Show the monthly average PM10 trend for Manesar from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Manesar'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Manesar (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Manesar'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Manesar (2019–2024)', width=600, height=300)
     return chart


</code>
"
2765,specific_pattern,Show a cumulative area chart of PM2.5 readings for Ujjain across 2019.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Ujjain') & (data['Timestamp'].dt.year == 2019)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Ujjain 2019', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Ujjain') & (data['Timestamp'].dt.year == 2019)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Ujjain 2019', width=600, height=300)
     return chart


</code>
"
2766,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Chandigarh, Madhya Pradesh, and Delhi across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Chandigarh', 'Madhya Pradesh', 'Delhi']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Chandigarh', 'Madhya Pradesh', 'Delhi']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
2767,spatial_aggregation,"Show the top 8 states by average PM10 in 2022 as a bar chart, sorted in descending order.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2022]
    df = df.groupby('state')['PM10'].mean().reset_index().dropna()
    df = df.nlargest(8, 'PM10')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Top 8 States by Average PM10 in 2022', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2022]
     df = df.groupby('state')['PM10'].mean().reset_index().dropna()
     df = df.nlargest(8, 'PM10')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Top 8 States by Average PM10 in 2022', width=500, height=300)
     return chart


</code>
"
2768,temporal_aggregation,Show the monthly average PM2.5 for Chengalpattu in 2020 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Chengalpattu') & (data['Timestamp'].dt.year == 2020)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Chengalpattu 2020', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Chengalpattu') & (data['Timestamp'].dt.year == 2020)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Chengalpattu 2020', width=450, height=280)


</code>
"
2769,temporal_aggregation,Show the monthly average PM2.5 for Lucknow in 2024 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Lucknow') & (data['Timestamp'].dt.year == 2024)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Lucknow 2024', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Lucknow') & (data['Timestamp'].dt.year == 2024)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Lucknow 2024', width=450, height=280)


</code>
"
2770,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Haryana, Manipur, and Meghalaya from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Haryana', 'Manipur', 'Meghalaya']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Haryana vs Manipur vs Meghalaya', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Haryana', 'Manipur', 'Meghalaya']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Haryana vs Manipur vs Meghalaya', width=550, height=320)
     return chart


</code>
"
2771,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Madhya Pradesh, Arunachal Pradesh, and West Bengal across 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Madhya Pradesh', 'Arunachal Pradesh', 'West Bengal']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Madhya Pradesh', 'Arunachal Pradesh', 'West Bengal']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
     return chart


</code>
"
2772,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Chandigarh, Tripura, and Punjab across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Chandigarh', 'Tripura', 'Punjab']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Chandigarh', 'Tripura', 'Punjab']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
2773,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Nagaland, Bihar, and Andhra Pradesh in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Nagaland', 'Bihar', 'Andhra Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Nagaland, Bihar, UP – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Nagaland', 'Bihar', 'Andhra Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Nagaland, Bihar, UP – 2022', width=550, height=320)
     return chart


</code>
"
2774,spatial_aggregation,Plot the top 14 states by average PM2.5 in 2017 as a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2017]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nlargest(14, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 14 States by Average PM2.5 in 2017', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2017]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nlargest(14, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 14 States by Average PM2.5 in 2017', width=500, height=300)
     return chart


</code>
"
2775,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Maharashtra, Puducherry, and Delhi in 2018 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Maharashtra', 'Puducherry', 'Delhi']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2018)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Maharashtra, Puducherry, UP – 2018', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Maharashtra', 'Puducherry', 'Delhi']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2018)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Maharashtra, Puducherry, UP – 2018', width=550, height=320)
     return chart


</code>
"
2776,specific_pattern,Plot the rolling 30-day average PM2.5 for West Bengal in 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'West Bengal') & (data['Timestamp'].dt.year == 2022)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – West Bengal 2022', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'West Bengal') & (data['Timestamp'].dt.year == 2022)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – West Bengal 2022', width=600, height=300)


</code>
"
2777,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Haryana, Gujarat, and Karnataka across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Haryana', 'Gujarat', 'Karnataka']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Haryana', 'Gujarat', 'Karnataka']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
2778,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Gujarat, Bihar, and Kerala from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Gujarat', 'Bihar', 'Kerala']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Gujarat vs Bihar vs Kerala', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Gujarat', 'Bihar', 'Kerala']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Gujarat vs Bihar vs Kerala', width=550, height=320)
     return chart


</code>
"
2779,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Uttarakhand, Assam, and Bihar in 2021 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Uttarakhand', 'Assam', 'Bihar']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Uttarakhand, Assam, UP – 2021', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Uttarakhand', 'Assam', 'Bihar']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Uttarakhand, Assam, UP – 2021', width=550, height=320)
     return chart


</code>
"
2780,temporal_aggregation,Show the monthly average PM2.5 for Jorapokhar in 2023 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Jorapokhar') & (data['Timestamp'].dt.year == 2023)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Jorapokhar 2023', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Jorapokhar') & (data['Timestamp'].dt.year == 2023)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Jorapokhar 2023', width=450, height=280)


</code>
"
2781,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Andhra Pradesh, Kerala, and Madhya Pradesh in 2019 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Andhra Pradesh', 'Kerala', 'Madhya Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Andhra Pradesh, Kerala, UP – 2019', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Andhra Pradesh', 'Kerala', 'Madhya Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Andhra Pradesh, Kerala, UP – 2019', width=550, height=320)
     return chart


</code>
"
2782,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Chandigarh, Arunachal Pradesh, and Punjab across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Chandigarh', 'Arunachal Pradesh', 'Punjab']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Chandigarh', 'Arunachal Pradesh', 'Punjab']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
2783,temporal_aggregation,Show the monthly average PM10 trend for Thane from 2017 to 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Thane'].copy()
    df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Thane (2017–2022)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Thane'].copy()
     df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Thane (2017–2022)', width=600, height=300)
     return chart


</code>
"
2784,specific_pattern,Show a cumulative area chart of PM2.5 readings for Chikkaballapur across 2019.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Chikkaballapur') & (data['Timestamp'].dt.year == 2019)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Chikkaballapur 2019', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Chikkaballapur') & (data['Timestamp'].dt.year == 2019)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Chikkaballapur 2019', width=600, height=300)
     return chart


</code>
"
2785,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Jharkhand, Andhra Pradesh, and Madhya Pradesh from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Jharkhand', 'Andhra Pradesh', 'Madhya Pradesh']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Jharkhand vs Andhra Pradesh vs Madhya Pradesh', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Jharkhand', 'Andhra Pradesh', 'Madhya Pradesh']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Jharkhand vs Andhra Pradesh vs Madhya Pradesh', width=550, height=320)
     return chart


</code>
"
2786,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Arunachal Pradesh, Kerala, and Haryana from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Arunachal Pradesh', 'Kerala', 'Haryana']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Arunachal Pradesh vs Kerala vs Haryana', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Arunachal Pradesh', 'Kerala', 'Haryana']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Arunachal Pradesh vs Kerala vs Haryana', width=550, height=320)
     return chart


</code>
"
2787,temporal_aggregation,Show the monthly average PM2.5 for Puducherry in 2018 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Puducherry') & (data['Timestamp'].dt.year == 2018)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Puducherry 2018', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Puducherry') & (data['Timestamp'].dt.year == 2018)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Puducherry 2018', width=450, height=280)


</code>
"
2788,temporal_aggregation,Show the monthly average PM10 trend for Kaithal from 2017 to 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Kaithal'].copy()
    df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Kaithal (2017–2022)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Kaithal'].copy()
     df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Kaithal (2017–2022)', width=600, height=300)
     return chart


</code>
"
2789,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Gujarat, Telangana, and West Bengal across 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Gujarat', 'Telangana', 'West Bengal']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Gujarat', 'Telangana', 'West Bengal']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
     return chart


</code>
"
2790,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Kolkata, Baghpat, and Nanded in 2019 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Kolkata', 'Baghpat', 'Nanded']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Kolkata vs Baghpat vs Nanded – 2019', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Kolkata', 'Baghpat', 'Nanded']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Kolkata vs Baghpat vs Nanded – 2019', width=550, height=320)
     return chart


</code>
"
2791,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Tripura, Himachal Pradesh, and Odisha from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Tripura', 'Himachal Pradesh', 'Odisha']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Tripura vs Himachal Pradesh vs Odisha', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Tripura', 'Himachal Pradesh', 'Odisha']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Tripura vs Himachal Pradesh vs Odisha', width=550, height=320)
     return chart


</code>
"
2792,spatial_aggregation,"Show the top 14 states by average PM10 in 2017 as a bar chart, sorted in descending order.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2017]
    df = df.groupby('state')['PM10'].mean().reset_index().dropna()
    df = df.nlargest(14, 'PM10')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Top 14 States by Average PM10 in 2017', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2017]
     df = df.groupby('state')['PM10'].mean().reset_index().dropna()
     df = df.nlargest(14, 'PM10')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Top 14 States by Average PM10 in 2017', width=500, height=300)
     return chart


</code>
"
2793,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Maharashtra, Jharkhand, and Rajasthan in 2017 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Maharashtra', 'Jharkhand', 'Rajasthan']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2017)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Maharashtra, Jharkhand, UP – 2017', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Maharashtra', 'Jharkhand', 'Rajasthan']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2017)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Maharashtra, Jharkhand, UP – 2017', width=550, height=320)
     return chart


</code>
"
2794,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Pithampur, Virudhunagar, and Meerut in 2018 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Pithampur', 'Virudhunagar', 'Meerut']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2018)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Pithampur vs Virudhunagar vs Meerut – 2018', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Pithampur', 'Virudhunagar', 'Meerut']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2018)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Pithampur vs Virudhunagar vs Meerut – 2018', width=550, height=320)
     return chart


</code>
"
2795,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Chhattisgarh stations in 2021, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Chhattisgarh') & (data['Timestamp'].dt.year == 2021)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Chhattisgarh Stations 2021', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Chhattisgarh') & (data['Timestamp'].dt.year == 2021)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Chhattisgarh Stations 2021', width=450, height=350)


</code>
"
2796,specific_pattern,Plot the rolling 30-day average PM2.5 for Telangana in 2018 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Telangana') & (data['Timestamp'].dt.year == 2018)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Telangana 2018', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Telangana') & (data['Timestamp'].dt.year == 2018)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Telangana 2018', width=600, height=300)


</code>
"
2797,specific_pattern,Show a cumulative area chart of PM2.5 readings for Palkalaiperur across 2024.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Palkalaiperur') & (data['Timestamp'].dt.year == 2024)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Palkalaiperur 2024', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Palkalaiperur') & (data['Timestamp'].dt.year == 2024)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Palkalaiperur 2024', width=600, height=300)
     return chart


</code>
"
2798,temporal_aggregation,Show the monthly average PM10 trend for Bettiah from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Bettiah'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Bettiah (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Bettiah'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Bettiah (2019–2024)', width=600, height=300)
     return chart


</code>
"
2799,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Tamil Nadu stations in 2017, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Tamil Nadu') & (data['Timestamp'].dt.year == 2017)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Tamil Nadu Stations 2017', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Tamil Nadu') & (data['Timestamp'].dt.year == 2017)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Tamil Nadu Stations 2017', width=450, height=350)


</code>
"
2800,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Jind, Dewas, and Moradabad in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Jind', 'Dewas', 'Moradabad']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Jind vs Dewas vs Moradabad – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Jind', 'Dewas', 'Moradabad']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Jind vs Dewas vs Moradabad – 2022', width=550, height=320)
     return chart


</code>
"
2801,temporal_aggregation,Show the monthly average PM2.5 for Vellore in 2022 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Vellore') & (data['Timestamp'].dt.year == 2022)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Vellore 2022', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Vellore') & (data['Timestamp'].dt.year == 2022)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Vellore 2022', width=450, height=280)


</code>
"
2802,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Himachal Pradesh, Jammu and Kashmir, and Gujarat across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Himachal Pradesh', 'Jammu and Kashmir', 'Gujarat']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Himachal Pradesh', 'Jammu and Kashmir', 'Gujarat']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
2803,specific_pattern,Plot the rolling 30-day average PM2.5 for Tamil Nadu in 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Tamil Nadu') & (data['Timestamp'].dt.year == 2022)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Tamil Nadu 2022', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Tamil Nadu') & (data['Timestamp'].dt.year == 2022)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Tamil Nadu 2022', width=600, height=300)


</code>
"
2804,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Bihar Sharif, Mangalore, and Ambala in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Bihar Sharif', 'Mangalore', 'Ambala']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Bihar Sharif vs Mangalore vs Ambala – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Bihar Sharif', 'Mangalore', 'Ambala']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Bihar Sharif vs Mangalore vs Ambala – 2024', width=550, height=320)
     return chart


</code>
"
2805,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Bihar, Andhra Pradesh, and Punjab in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Bihar', 'Andhra Pradesh', 'Punjab']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Bihar, Andhra Pradesh, UP – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Bihar', 'Andhra Pradesh', 'Punjab']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Bihar, Andhra Pradesh, UP – 2024', width=550, height=320)
     return chart


</code>
"
2806,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Bihar, Chandigarh, and Punjab in 2021 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Bihar', 'Chandigarh', 'Punjab']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Bihar, Chandigarh, UP – 2021', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Bihar', 'Chandigarh', 'Punjab']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Bihar, Chandigarh, UP – 2021', width=550, height=320)
     return chart


</code>
"
2807,temporal_aggregation,Show the monthly average PM2.5 for Baripada in 2018 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Baripada') & (data['Timestamp'].dt.year == 2018)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Baripada 2018', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Baripada') & (data['Timestamp'].dt.year == 2018)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Baripada 2018', width=450, height=280)


</code>
"
2808,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Punjab, Sikkim, and Maharashtra in 2017 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Punjab', 'Sikkim', 'Maharashtra']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2017)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Punjab, Sikkim, UP – 2017', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Punjab', 'Sikkim', 'Maharashtra']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2017)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Punjab, Sikkim, UP – 2017', width=550, height=320)
     return chart


</code>
"
2809,specific_pattern,Plot the rolling 30-day average PM2.5 for Nagaland in 2023 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Nagaland') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Nagaland 2023', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Nagaland') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Nagaland 2023', width=600, height=300)


</code>
"
2810,temporal_aggregation,Plot the weekly average PM2.5 for Mandideep in 2020 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Mandideep') & (data['Timestamp'].dt.year == 2020)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Mandideep 2020', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Mandideep') & (data['Timestamp'].dt.year == 2020)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Mandideep 2020', width=600, height=300)
     return chart


</code>
"
2811,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Jammu and Kashmir, Chhattisgarh, and Manipur from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Jammu and Kashmir', 'Chhattisgarh', 'Manipur']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Jammu and Kashmir vs Chhattisgarh vs Manipur', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Jammu and Kashmir', 'Chhattisgarh', 'Manipur']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Jammu and Kashmir vs Chhattisgarh vs Manipur', width=550, height=320)
     return chart


</code>
"
2812,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Madhya Pradesh, West Bengal, and Bihar from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Madhya Pradesh', 'West Bengal', 'Bihar']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Madhya Pradesh vs West Bengal vs Bihar', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Madhya Pradesh', 'West Bengal', 'Bihar']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Madhya Pradesh vs West Bengal vs Bihar', width=550, height=320)
     return chart


</code>
"
2813,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Udaipur, Chamarajanagar, and Sirsa in 2020 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Udaipur', 'Chamarajanagar', 'Sirsa']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2020)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Udaipur vs Chamarajanagar vs Sirsa – 2020', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Udaipur', 'Chamarajanagar', 'Sirsa']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2020)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Udaipur vs Chamarajanagar vs Sirsa – 2020', width=550, height=320)
     return chart


</code>
"
2814,temporal_aggregation,Show the monthly average PM2.5 for Mandi Gobindgarh in 2020 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Mandi Gobindgarh') & (data['Timestamp'].dt.year == 2020)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Mandi Gobindgarh 2020', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Mandi Gobindgarh') & (data['Timestamp'].dt.year == 2020)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Mandi Gobindgarh 2020', width=450, height=280)


</code>
"
2815,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Arunachal Pradesh, Uttar Pradesh, and Madhya Pradesh from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Arunachal Pradesh', 'Uttar Pradesh', 'Madhya Pradesh']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Arunachal Pradesh vs Uttar Pradesh vs Madhya Pradesh', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Arunachal Pradesh', 'Uttar Pradesh', 'Madhya Pradesh']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Arunachal Pradesh vs Uttar Pradesh vs Madhya Pradesh', width=550, height=320)
     return chart


</code>
"
2816,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Tripura stations in 2021, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Tripura') & (data['Timestamp'].dt.year == 2021)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Tripura Stations 2021', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Tripura') & (data['Timestamp'].dt.year == 2021)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Tripura Stations 2021', width=450, height=350)


</code>
"
2817,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Chikkaballapur, Gangtok, and Khurja in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Chikkaballapur', 'Gangtok', 'Khurja']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Chikkaballapur vs Gangtok vs Khurja – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Chikkaballapur', 'Gangtok', 'Khurja']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Chikkaballapur vs Gangtok vs Khurja – 2023', width=550, height=320)
     return chart


</code>
"
2818,temporal_aggregation,Plot the weekly average PM2.5 for Fatehabad in 2020 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Fatehabad') & (data['Timestamp'].dt.year == 2020)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Fatehabad 2020', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Fatehabad') & (data['Timestamp'].dt.year == 2020)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Fatehabad 2020', width=600, height=300)
     return chart


</code>
"
2819,temporal_aggregation,Plot the weekly average PM2.5 for Muzaffarnagar in 2018 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Muzaffarnagar') & (data['Timestamp'].dt.year == 2018)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Muzaffarnagar 2018', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Muzaffarnagar') & (data['Timestamp'].dt.year == 2018)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Muzaffarnagar 2018', width=600, height=300)
     return chart


</code>
"
2820,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Hajipur, Karwar, and Davanagere in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Hajipur', 'Karwar', 'Davanagere']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Hajipur vs Karwar vs Davanagere – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Hajipur', 'Karwar', 'Davanagere']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Hajipur vs Karwar vs Davanagere – 2024', width=550, height=320)
     return chart


</code>
"
2821,specific_pattern,Show a cumulative area chart of PM2.5 readings for Bundi across 2023.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Bundi') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Bundi 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Bundi') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Bundi 2023', width=600, height=300)
     return chart


</code>
"
2822,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Mizoram, Himachal Pradesh, and Chandigarh from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Mizoram', 'Himachal Pradesh', 'Chandigarh']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Mizoram vs Himachal Pradesh vs Chandigarh', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Mizoram', 'Himachal Pradesh', 'Chandigarh']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Mizoram vs Himachal Pradesh vs Chandigarh', width=550, height=320)
     return chart


</code>
"
2823,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Andhra Pradesh, Uttarakhand, and Himachal Pradesh from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Andhra Pradesh', 'Uttarakhand', 'Himachal Pradesh']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Andhra Pradesh vs Uttarakhand vs Himachal Pradesh', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Andhra Pradesh', 'Uttarakhand', 'Himachal Pradesh']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Andhra Pradesh vs Uttarakhand vs Himachal Pradesh', width=550, height=320)
     return chart


</code>
"
2824,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Telangana, Jammu and Kashmir, and Andhra Pradesh across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Telangana', 'Jammu and Kashmir', 'Andhra Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Telangana', 'Jammu and Kashmir', 'Andhra Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
2825,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Arunachal Pradesh, Bihar, and Chandigarh from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Arunachal Pradesh', 'Bihar', 'Chandigarh']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Arunachal Pradesh vs Bihar vs Chandigarh', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Arunachal Pradesh', 'Bihar', 'Chandigarh']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Arunachal Pradesh vs Bihar vs Chandigarh', width=550, height=320)
     return chart


</code>
"
2826,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Jammu and Kashmir, Gujarat, and Gujarat across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Jammu and Kashmir', 'Gujarat', 'Gujarat']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Jammu and Kashmir', 'Gujarat', 'Gujarat']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
2827,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Mizoram, Sikkim, and Jharkhand from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Mizoram', 'Sikkim', 'Jharkhand']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Mizoram vs Sikkim vs Jharkhand', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Mizoram', 'Sikkim', 'Jharkhand']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Mizoram vs Sikkim vs Jharkhand', width=550, height=320)
     return chart


</code>
"
2828,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Sagar, Baghpat, and Nagpur in 2018 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Sagar', 'Baghpat', 'Nagpur']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2018)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Sagar vs Baghpat vs Nagpur – 2018', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Sagar', 'Baghpat', 'Nagpur']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2018)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Sagar vs Baghpat vs Nagpur – 2018', width=550, height=320)
     return chart


</code>
"
2829,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Madhya Pradesh, Arunachal Pradesh, and Chandigarh across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Madhya Pradesh', 'Arunachal Pradesh', 'Chandigarh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Madhya Pradesh', 'Arunachal Pradesh', 'Chandigarh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
2830,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Hubballi, Tumakuru, and Sikar in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Hubballi', 'Tumakuru', 'Sikar']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Hubballi vs Tumakuru vs Sikar – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Hubballi', 'Tumakuru', 'Sikar']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Hubballi vs Tumakuru vs Sikar – 2024', width=550, height=320)
     return chart


</code>
"
2831,temporal_aggregation,Plot the weekly average PM2.5 for Singrauli in 2017 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Singrauli') & (data['Timestamp'].dt.year == 2017)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Singrauli 2017', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Singrauli') & (data['Timestamp'].dt.year == 2017)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Singrauli 2017', width=600, height=300)
     return chart


</code>
"
2832,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Manipur, Sikkim, and Andhra Pradesh from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Manipur', 'Sikkim', 'Andhra Pradesh']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Manipur vs Sikkim vs Andhra Pradesh', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Manipur', 'Sikkim', 'Andhra Pradesh']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Manipur vs Sikkim vs Andhra Pradesh', width=550, height=320)
     return chart


</code>
"
2833,temporal_aggregation,Show the monthly average PM10 trend for Ramanagara from 2017 to 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Ramanagara'].copy()
    df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Ramanagara (2017–2022)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Ramanagara'].copy()
     df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Ramanagara (2017–2022)', width=600, height=300)
     return chart


</code>
"
2834,temporal_aggregation,Plot the weekly average PM2.5 for Pune in 2021 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Pune') & (data['Timestamp'].dt.year == 2021)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Pune 2021', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Pune') & (data['Timestamp'].dt.year == 2021)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Pune 2021', width=600, height=300)
     return chart


</code>
"
2835,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Gandhinagar, Kota, and Aizawl in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Gandhinagar', 'Kota', 'Aizawl']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Gandhinagar vs Kota vs Aizawl – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Gandhinagar', 'Kota', 'Aizawl']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Gandhinagar vs Kota vs Aizawl – 2024', width=550, height=320)
     return chart


</code>
"
2836,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Manipur, Tamil Nadu, and Haryana in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Manipur', 'Tamil Nadu', 'Haryana']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Manipur, Tamil Nadu, UP – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Manipur', 'Tamil Nadu', 'Haryana']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Manipur, Tamil Nadu, UP – 2024', width=550, height=320)
     return chart


</code>
"
2837,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Arunachal Pradesh, Himachal Pradesh, and Sikkim across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Arunachal Pradesh', 'Himachal Pradesh', 'Sikkim']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Arunachal Pradesh', 'Himachal Pradesh', 'Sikkim']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
2838,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Arunachal Pradesh, Chhattisgarh, and Jharkhand from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Arunachal Pradesh', 'Chhattisgarh', 'Jharkhand']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Arunachal Pradesh vs Chhattisgarh vs Jharkhand', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Arunachal Pradesh', 'Chhattisgarh', 'Jharkhand']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Arunachal Pradesh vs Chhattisgarh vs Jharkhand', width=550, height=320)
     return chart


</code>
"
2839,temporal_aggregation,Plot the weekly average PM2.5 for Durgapur in 2021 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Durgapur') & (data['Timestamp'].dt.year == 2021)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Durgapur 2021', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Durgapur') & (data['Timestamp'].dt.year == 2021)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Durgapur 2021', width=600, height=300)
     return chart


</code>
"
2840,temporal_aggregation,Plot the weekly average PM2.5 for Patiala in 2018 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Patiala') & (data['Timestamp'].dt.year == 2018)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Patiala 2018', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Patiala') & (data['Timestamp'].dt.year == 2018)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Patiala 2018', width=600, height=300)
     return chart


</code>
"
2841,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Bundi, Hisar, and Nagpur in 2019 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Bundi', 'Hisar', 'Nagpur']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Bundi vs Hisar vs Nagpur – 2019', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Bundi', 'Hisar', 'Nagpur']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Bundi vs Hisar vs Nagpur – 2019', width=550, height=320)
     return chart


</code>
"
2842,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Meghalaya, Andhra Pradesh, and Gujarat in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Meghalaya', 'Andhra Pradesh', 'Gujarat']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Meghalaya, Andhra Pradesh, UP – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Meghalaya', 'Andhra Pradesh', 'Gujarat']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Meghalaya, Andhra Pradesh, UP – 2022', width=550, height=320)
     return chart


</code>
"
2843,temporal_aggregation,Show the monthly average PM10 trend for Davanagere from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Davanagere'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Davanagere (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Davanagere'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Davanagere (2019–2024)', width=600, height=300)
     return chart


</code>
"
2844,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Chandigarh, Kerala, and Telangana across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Chandigarh', 'Kerala', 'Telangana']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Chandigarh', 'Kerala', 'Telangana']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
2845,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Ratlam, Amritsar, and Nagaur in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Ratlam', 'Amritsar', 'Nagaur']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Ratlam vs Amritsar vs Nagaur – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Ratlam', 'Amritsar', 'Nagaur']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Ratlam vs Amritsar vs Nagaur – 2022', width=550, height=320)
     return chart


</code>
"
2846,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Telangana, Kerala, and Andhra Pradesh across 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Telangana', 'Kerala', 'Andhra Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Telangana', 'Kerala', 'Andhra Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
     return chart


</code>
"
2847,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Tripura stations in 2024, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Tripura') & (data['Timestamp'].dt.year == 2024)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Tripura Stations 2024', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Tripura') & (data['Timestamp'].dt.year == 2024)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Tripura Stations 2024', width=450, height=350)


</code>
"
2848,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Haryana, Arunachal Pradesh, and Meghalaya in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Haryana', 'Arunachal Pradesh', 'Meghalaya']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Haryana, Arunachal Pradesh, UP – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Haryana', 'Arunachal Pradesh', 'Meghalaya']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Haryana, Arunachal Pradesh, UP – 2022', width=550, height=320)
     return chart


</code>
"
2849,temporal_aggregation,Show the monthly average PM2.5 for Gurugram in 2018 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Gurugram') & (data['Timestamp'].dt.year == 2018)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Gurugram 2018', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Gurugram') & (data['Timestamp'].dt.year == 2018)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Gurugram 2018', width=450, height=280)


</code>
"
2850,spatio_temporal_aggregation,Show a heatmap of average PM2.5 for the top 10 most polluted states by month for 2023.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    top8 = data.groupby('state')['PM2.5'].mean().nlargest(10).index.tolist()
    df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_rect().encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('state:N', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
        tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Heatmap – Top 10 Polluted States by Month (2023)', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     top8 = data.groupby('state')['PM2.5'].mean().nlargest(10).index.tolist()
     df = data[(data['state'].isin(top8)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['state','Month'])['PM2.5'].mean().reset_index().dropna()
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_rect().encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('state:N', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), title='PM2.5'),
         tooltip=['state:N','MonthName:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Heatmap – Top 10 Polluted States by Month (2023)', width=500, height=300)
     return chart


</code>
"
2851,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Jammu and Kashmir, Tamil Nadu, and Himachal Pradesh from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Jammu and Kashmir', 'Tamil Nadu', 'Himachal Pradesh']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Jammu and Kashmir vs Tamil Nadu vs Himachal Pradesh', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Jammu and Kashmir', 'Tamil Nadu', 'Himachal Pradesh']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Jammu and Kashmir vs Tamil Nadu vs Himachal Pradesh', width=550, height=320)
     return chart


</code>
"
2852,specific_pattern,Plot the rolling 30-day average PM2.5 for Gujarat in 2017 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Gujarat') & (data['Timestamp'].dt.year == 2017)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Gujarat 2017', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Gujarat') & (data['Timestamp'].dt.year == 2017)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Gujarat 2017', width=600, height=300)


</code>
"
2853,specific_pattern,Show a cumulative area chart of PM2.5 readings for Guwahati across 2024.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Guwahati') & (data['Timestamp'].dt.year == 2024)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Guwahati 2024', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Guwahati') & (data['Timestamp'].dt.year == 2024)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Guwahati 2024', width=600, height=300)
     return chart


</code>
"
2854,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Chhattisgarh, Uttarakhand, and Mizoram in 2021 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Chhattisgarh', 'Uttarakhand', 'Mizoram']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Chhattisgarh, Uttarakhand, UP – 2021', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Chhattisgarh', 'Uttarakhand', 'Mizoram']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Chhattisgarh, Uttarakhand, UP – 2021', width=550, height=320)
     return chart


</code>
"
2855,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Andhra Pradesh, Meghalaya, and Tamil Nadu across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Andhra Pradesh', 'Meghalaya', 'Tamil Nadu']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Andhra Pradesh', 'Meghalaya', 'Tamil Nadu']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
2856,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Delhi, Karnataka, and Assam in 2021 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Delhi', 'Karnataka', 'Assam']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Delhi, Karnataka, UP – 2021', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Delhi', 'Karnataka', 'Assam']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Delhi, Karnataka, UP – 2021', width=550, height=320)
     return chart


</code>
"
2857,temporal_aggregation,Plot the weekly average PM2.5 for Mandikhera in 2020 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Mandikhera') & (data['Timestamp'].dt.year == 2020)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Mandikhera 2020', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Mandikhera') & (data['Timestamp'].dt.year == 2020)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Mandikhera 2020', width=600, height=300)
     return chart


</code>
"
2858,temporal_aggregation,Show the monthly average PM10 trend for Ooty from 2017 to 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Ooty'].copy()
    df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Ooty (2017–2022)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Ooty'].copy()
     df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Ooty (2017–2022)', width=600, height=300)
     return chart


</code>
"
2859,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Rajasthan, Chandigarh, and Karnataka from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Rajasthan', 'Chandigarh', 'Karnataka']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Rajasthan vs Chandigarh vs Karnataka', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Rajasthan', 'Chandigarh', 'Karnataka']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Rajasthan vs Chandigarh vs Karnataka', width=550, height=320)
     return chart


</code>
"
2860,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Chandigarh, Chandigarh, and Jammu and Kashmir across 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Chandigarh', 'Chandigarh', 'Jammu and Kashmir']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Chandigarh', 'Chandigarh', 'Jammu and Kashmir']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
     return chart


</code>
"
2861,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Andhra Pradesh, Chhattisgarh, and Mizoram from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Andhra Pradesh', 'Chhattisgarh', 'Mizoram']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Andhra Pradesh vs Chhattisgarh vs Mizoram', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Andhra Pradesh', 'Chhattisgarh', 'Mizoram']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Andhra Pradesh vs Chhattisgarh vs Mizoram', width=550, height=320)
     return chart


</code>
"
2862,temporal_aggregation,Show the monthly average PM10 trend for Bileipada from 2017 to 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Bileipada'].copy()
    df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Bileipada (2017–2022)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Bileipada'].copy()
     df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Bileipada (2017–2022)', width=600, height=300)
     return chart


</code>
"
2863,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Jhalawar, Nagaur, and Charkhi Dadri in 2020 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Jhalawar', 'Nagaur', 'Charkhi Dadri']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2020)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Jhalawar vs Nagaur vs Charkhi Dadri – 2020', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Jhalawar', 'Nagaur', 'Charkhi Dadri']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2020)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Jhalawar vs Nagaur vs Charkhi Dadri – 2020', width=550, height=320)
     return chart


</code>
"
2864,temporal_aggregation,Show the monthly average PM10 trend for Bhopal from 2017 to 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Bhopal'].copy()
    df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Bhopal (2017–2022)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Bhopal'].copy()
     df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Bhopal (2017–2022)', width=600, height=300)
     return chart


</code>
"
2865,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Haryana, Telangana, and Odisha in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Haryana', 'Telangana', 'Odisha']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Haryana, Telangana, UP – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Haryana', 'Telangana', 'Odisha']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Haryana, Telangana, UP – 2023', width=550, height=320)
     return chart


</code>
"
2866,specific_pattern,Plot the rolling 30-day average PM2.5 for Punjab in 2019 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Punjab') & (data['Timestamp'].dt.year == 2019)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Punjab 2019', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Punjab') & (data['Timestamp'].dt.year == 2019)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Punjab 2019', width=600, height=300)


</code>
"
2867,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Arunachal Pradesh, Manipur, and Uttar Pradesh across 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Arunachal Pradesh', 'Manipur', 'Uttar Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Arunachal Pradesh', 'Manipur', 'Uttar Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
     return chart


</code>
"
2868,temporal_aggregation,Show the monthly average PM2.5 for Ulhasnagar in 2024 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Ulhasnagar') & (data['Timestamp'].dt.year == 2024)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Ulhasnagar 2024', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Ulhasnagar') & (data['Timestamp'].dt.year == 2024)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Ulhasnagar 2024', width=450, height=280)


</code>
"
2869,specific_pattern,Show a cumulative area chart of PM2.5 readings for Shillong across 2021.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Shillong') & (data['Timestamp'].dt.year == 2021)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Shillong 2021', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Shillong') & (data['Timestamp'].dt.year == 2021)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Shillong 2021', width=600, height=300)
     return chart


</code>
"
2870,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Gujarat, Sikkim, and Jharkhand from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Gujarat', 'Sikkim', 'Jharkhand']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Gujarat vs Sikkim vs Jharkhand', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Gujarat', 'Sikkim', 'Jharkhand']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Gujarat vs Sikkim vs Jharkhand', width=550, height=320)
     return chart


</code>
"
2871,temporal_aggregation,Show the monthly average PM2.5 for Dungarpur in 2022 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Dungarpur') & (data['Timestamp'].dt.year == 2022)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Dungarpur 2022', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Dungarpur') & (data['Timestamp'].dt.year == 2022)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Dungarpur 2022', width=450, height=280)


</code>
"
2872,temporal_aggregation,Show the monthly average PM10 trend for Kochi from 2017 to 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Kochi'].copy()
    df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Kochi (2017–2022)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Kochi'].copy()
     df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Kochi (2017–2022)', width=600, height=300)
     return chart


</code>
"
2873,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Karnataka, Andhra Pradesh, and Tripura from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Karnataka', 'Andhra Pradesh', 'Tripura']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Karnataka vs Andhra Pradesh vs Tripura', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Karnataka', 'Andhra Pradesh', 'Tripura']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Karnataka vs Andhra Pradesh vs Tripura', width=550, height=320)
     return chart


</code>
"
2874,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Himachal Pradesh, Delhi, and Himachal Pradesh across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Himachal Pradesh', 'Delhi', 'Himachal Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Himachal Pradesh', 'Delhi', 'Himachal Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
2875,temporal_aggregation,Show the monthly average PM2.5 for Pratapgarh in 2019 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Pratapgarh') & (data['Timestamp'].dt.year == 2019)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Pratapgarh 2019', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Pratapgarh') & (data['Timestamp'].dt.year == 2019)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Pratapgarh 2019', width=450, height=280)


</code>
"
2876,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Tripura, Puducherry, and Gujarat across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Tripura', 'Puducherry', 'Gujarat']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Tripura', 'Puducherry', 'Gujarat']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
2877,temporal_aggregation,Show the monthly average PM2.5 for Pathardih in 2024 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Pathardih') & (data['Timestamp'].dt.year == 2024)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Pathardih 2024', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Pathardih') & (data['Timestamp'].dt.year == 2024)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Pathardih 2024', width=450, height=280)


</code>
"
2878,temporal_aggregation,Show the monthly average PM2.5 for Durgapur in 2020 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Durgapur') & (data['Timestamp'].dt.year == 2020)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Durgapur 2020', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Durgapur') & (data['Timestamp'].dt.year == 2020)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Durgapur 2020', width=450, height=280)


</code>
"
2879,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Baghpat, Eloor, and Khanna in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Baghpat', 'Eloor', 'Khanna']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Baghpat vs Eloor vs Khanna – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Baghpat', 'Eloor', 'Khanna']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Baghpat vs Eloor vs Khanna – 2022', width=550, height=320)
     return chart


</code>
"
2880,temporal_aggregation,Show the monthly average PM2.5 for Kohima in 2018 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Kohima') & (data['Timestamp'].dt.year == 2018)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Kohima 2018', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Kohima') & (data['Timestamp'].dt.year == 2018)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Kohima 2018', width=450, height=280)


</code>
"
2881,temporal_aggregation,Show a monthly bar chart of the number of days Meghalaya exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2020.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Meghalaya') & (data['Timestamp'].dt.year == 2020)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Meghalaya Exceeded WHO PM2.5 Guideline per Month – 2020', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Meghalaya') & (data['Timestamp'].dt.year == 2020)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Meghalaya Exceeded WHO PM2.5 Guideline per Month – 2020', width=500, height=300)
     return chart


</code>
"
2882,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Haryana, Kerala, and Jharkhand from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Haryana', 'Kerala', 'Jharkhand']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Haryana vs Kerala vs Jharkhand', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Haryana', 'Kerala', 'Jharkhand']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Haryana vs Kerala vs Jharkhand', width=550, height=320)
     return chart


</code>
"
2883,temporal_aggregation,Show the monthly average PM2.5 for Navi Mumbai in 2022 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Navi Mumbai') & (data['Timestamp'].dt.year == 2022)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Navi Mumbai 2022', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Navi Mumbai') & (data['Timestamp'].dt.year == 2022)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Navi Mumbai 2022', width=450, height=280)


</code>
"
2884,temporal_aggregation,Show the monthly average PM10 trend for Visakhapatnam from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Visakhapatnam'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Visakhapatnam (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Visakhapatnam'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Visakhapatnam (2019–2024)', width=600, height=300)
     return chart


</code>
"
2885,temporal_aggregation,Show the monthly average PM2.5 for Raichur in 2022 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Raichur') & (data['Timestamp'].dt.year == 2022)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Raichur 2022', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Raichur') & (data['Timestamp'].dt.year == 2022)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Raichur 2022', width=450, height=280)


</code>
"
2886,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Assam, Assam, and Meghalaya from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Assam', 'Assam', 'Meghalaya']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Assam vs Assam vs Meghalaya', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Assam', 'Assam', 'Meghalaya']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Assam vs Assam vs Meghalaya', width=550, height=320)
     return chart


</code>
"
2887,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Kolkata, Balasore, and Puducherry in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Kolkata', 'Balasore', 'Puducherry']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Kolkata vs Balasore vs Puducherry – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Kolkata', 'Balasore', 'Puducherry']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Kolkata vs Balasore vs Puducherry – 2023', width=550, height=320)
     return chart


</code>
"
2888,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Madhya Pradesh, West Bengal, and Manipur from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Madhya Pradesh', 'West Bengal', 'Manipur']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Madhya Pradesh vs West Bengal vs Manipur', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Madhya Pradesh', 'West Bengal', 'Manipur']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Madhya Pradesh vs West Bengal vs Manipur', width=550, height=320)
     return chart


</code>
"
2889,specific_pattern,Show a cumulative area chart of PM2.5 readings for Bulandshahr across 2018.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Bulandshahr') & (data['Timestamp'].dt.year == 2018)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Bulandshahr 2018', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Bulandshahr') & (data['Timestamp'].dt.year == 2018)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Bulandshahr 2018', width=600, height=300)
     return chart


</code>
"
2890,specific_pattern,Show a cumulative area chart of PM2.5 readings for Vrindavan across 2021.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Vrindavan') & (data['Timestamp'].dt.year == 2021)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Vrindavan 2021', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Vrindavan') & (data['Timestamp'].dt.year == 2021)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Vrindavan 2021', width=600, height=300)
     return chart


</code>
"
2891,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Punjab, Madhya Pradesh, and Gujarat from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Punjab', 'Madhya Pradesh', 'Gujarat']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Punjab vs Madhya Pradesh vs Gujarat', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Punjab', 'Madhya Pradesh', 'Gujarat']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Punjab vs Madhya Pradesh vs Gujarat', width=550, height=320)
     return chart


</code>
"
2892,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Karnataka, Andhra Pradesh, and Maharashtra in 2018 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Karnataka', 'Andhra Pradesh', 'Maharashtra']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2018)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Karnataka, Andhra Pradesh, UP – 2018', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Karnataka', 'Andhra Pradesh', 'Maharashtra']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2018)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Karnataka, Andhra Pradesh, UP – 2018', width=550, height=320)
     return chart


</code>
"
2893,temporal_aggregation,Plot the weekly average PM2.5 for Patna in 2021 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Patna') & (data['Timestamp'].dt.year == 2021)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Patna 2021', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Patna') & (data['Timestamp'].dt.year == 2021)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Patna 2021', width=600, height=300)
     return chart


</code>
"
2894,temporal_aggregation,Show the monthly average PM10 trend for Rohtak from 2017 to 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Rohtak'].copy()
    df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Rohtak (2017–2022)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Rohtak'].copy()
     df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Rohtak (2017–2022)', width=600, height=300)
     return chart


</code>
"
2895,specific_pattern,Show a cumulative area chart of PM2.5 readings for Bettiah across 2024.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Bettiah') & (data['Timestamp'].dt.year == 2024)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Bettiah 2024', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Bettiah') & (data['Timestamp'].dt.year == 2024)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Bettiah 2024', width=600, height=300)
     return chart


</code>
"
2896,specific_pattern,Plot the rolling 30-day average PM2.5 for Madhya Pradesh in 2023 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Madhya Pradesh') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Madhya Pradesh 2023', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Madhya Pradesh') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Madhya Pradesh 2023', width=600, height=300)


</code>
"
2897,temporal_aggregation,Plot the weekly average PM2.5 for Imphal in 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Imphal') & (data['Timestamp'].dt.year == 2024)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Imphal 2024', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Imphal') & (data['Timestamp'].dt.year == 2024)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Imphal 2024', width=600, height=300)
     return chart


</code>
"
2898,temporal_aggregation,Show a monthly bar chart of the number of days Mizoram exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2021.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Mizoram') & (data['Timestamp'].dt.year == 2021)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Mizoram Exceeded WHO PM2.5 Guideline per Month – 2021', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Mizoram') & (data['Timestamp'].dt.year == 2021)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Mizoram Exceeded WHO PM2.5 Guideline per Month – 2021', width=500, height=300)
     return chart


</code>
"
2899,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Andhra Pradesh stations in 2020, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Andhra Pradesh') & (data['Timestamp'].dt.year == 2020)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Andhra Pradesh Stations 2020', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Andhra Pradesh') & (data['Timestamp'].dt.year == 2020)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Andhra Pradesh Stations 2020', width=450, height=350)


</code>
"
2900,temporal_aggregation,Show a monthly bar chart of the number of days Gujarat exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2020.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Gujarat') & (data['Timestamp'].dt.year == 2020)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Gujarat Exceeded WHO PM2.5 Guideline per Month – 2020', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Gujarat') & (data['Timestamp'].dt.year == 2020)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Gujarat Exceeded WHO PM2.5 Guideline per Month – 2020', width=500, height=300)
     return chart


</code>
"
2901,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Punjab, Arunachal Pradesh, and Chandigarh in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Punjab', 'Arunachal Pradesh', 'Chandigarh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Punjab, Arunachal Pradesh, UP – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Punjab', 'Arunachal Pradesh', 'Chandigarh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Punjab, Arunachal Pradesh, UP – 2024', width=550, height=320)
     return chart


</code>
"
2902,specific_pattern,Show a cumulative area chart of PM2.5 readings for Bengaluru across 2023.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Bengaluru') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Bengaluru 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Bengaluru') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Bengaluru 2023', width=600, height=300)
     return chart


</code>
"
2903,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Puducherry, Jammu and Kashmir, and Bihar from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Puducherry', 'Jammu and Kashmir', 'Bihar']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Puducherry vs Jammu and Kashmir vs Bihar', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Puducherry', 'Jammu and Kashmir', 'Bihar']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Puducherry vs Jammu and Kashmir vs Bihar', width=550, height=320)
     return chart


</code>
"
2904,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Bhopal, Tensa, and Chittorgarh in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Bhopal', 'Tensa', 'Chittorgarh']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Bhopal vs Tensa vs Chittorgarh – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Bhopal', 'Tensa', 'Chittorgarh']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Bhopal vs Tensa vs Chittorgarh – 2024', width=550, height=320)
     return chart


</code>
"
2905,temporal_aggregation,Show the monthly average PM2.5 for Baghpat in 2020 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Baghpat') & (data['Timestamp'].dt.year == 2020)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Baghpat 2020', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Baghpat') & (data['Timestamp'].dt.year == 2020)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Baghpat 2020', width=450, height=280)


</code>
"
2906,temporal_aggregation,Plot the weekly average PM2.5 for Thane in 2017 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Thane') & (data['Timestamp'].dt.year == 2017)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Thane 2017', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Thane') & (data['Timestamp'].dt.year == 2017)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Thane 2017', width=600, height=300)
     return chart


</code>
"
2907,specific_pattern,Plot the rolling 30-day average PM2.5 for Rajasthan in 2017 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Rajasthan') & (data['Timestamp'].dt.year == 2017)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Rajasthan 2017', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Rajasthan') & (data['Timestamp'].dt.year == 2017)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Rajasthan 2017', width=600, height=300)


</code>
"
2908,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Puducherry stations in 2018, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Puducherry') & (data['Timestamp'].dt.year == 2018)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Puducherry Stations 2018', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Puducherry') & (data['Timestamp'].dt.year == 2018)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Puducherry Stations 2018', width=450, height=350)


</code>
"
2909,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Lucknow, Hapur, and Kalyan in 2019 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Lucknow', 'Hapur', 'Kalyan']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Lucknow vs Hapur vs Kalyan – 2019', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Lucknow', 'Hapur', 'Kalyan']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Lucknow vs Hapur vs Kalyan – 2019', width=550, height=320)
     return chart


</code>
"
2910,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Ahmednagar, Siliguri, and Tiruchirappalli in 2019 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Ahmednagar', 'Siliguri', 'Tiruchirappalli']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Ahmednagar vs Siliguri vs Tiruchirappalli – 2019', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Ahmednagar', 'Siliguri', 'Tiruchirappalli']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Ahmednagar vs Siliguri vs Tiruchirappalli – 2019', width=550, height=320)
     return chart


</code>
"
2911,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Madhya Pradesh, Gujarat, and Jharkhand from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Madhya Pradesh', 'Gujarat', 'Jharkhand']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Madhya Pradesh vs Gujarat vs Jharkhand', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Madhya Pradesh', 'Gujarat', 'Jharkhand']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Madhya Pradesh vs Gujarat vs Jharkhand', width=550, height=320)
     return chart


</code>
"
2912,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Punjab, Kerala, and Sikkim from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Punjab', 'Kerala', 'Sikkim']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Punjab vs Kerala vs Sikkim', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Punjab', 'Kerala', 'Sikkim']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Punjab vs Kerala vs Sikkim', width=550, height=320)
     return chart


</code>
"
2913,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Rajamahendravaram, Tumakuru, and Bhagalpur in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Rajamahendravaram', 'Tumakuru', 'Bhagalpur']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Rajamahendravaram vs Tumakuru vs Bhagalpur – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Rajamahendravaram', 'Tumakuru', 'Bhagalpur']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Rajamahendravaram vs Tumakuru vs Bhagalpur – 2022', width=550, height=320)
     return chart


</code>
"
2914,temporal_aggregation,Show the monthly average PM10 trend for Jind from 2017 to 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Jind'].copy()
    df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Jind (2017–2022)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Jind'].copy()
     df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Jind (2017–2022)', width=600, height=300)
     return chart


</code>
"
2915,temporal_aggregation,Show the monthly average PM2.5 for Naharlagun in 2019 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Naharlagun') & (data['Timestamp'].dt.year == 2019)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Naharlagun 2019', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Naharlagun') & (data['Timestamp'].dt.year == 2019)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Naharlagun 2019', width=450, height=280)


</code>
"
2916,temporal_aggregation,Plot the weekly average PM2.5 for Vijayawada in 2019 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Vijayawada') & (data['Timestamp'].dt.year == 2019)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Vijayawada 2019', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Vijayawada') & (data['Timestamp'].dt.year == 2019)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Vijayawada 2019', width=600, height=300)
     return chart


</code>
"
2917,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Ludhiana, Bhubaneswar, and Jalna in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Ludhiana', 'Bhubaneswar', 'Jalna']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Ludhiana vs Bhubaneswar vs Jalna – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Ludhiana', 'Bhubaneswar', 'Jalna']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Ludhiana vs Bhubaneswar vs Jalna – 2022', width=550, height=320)
     return chart


</code>
"
2918,temporal_aggregation,Show the monthly average PM2.5 for Tirupur in 2022 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Tirupur') & (data['Timestamp'].dt.year == 2022)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Tirupur 2022', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Tirupur') & (data['Timestamp'].dt.year == 2022)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Tirupur 2022', width=450, height=280)


</code>
"
2919,temporal_aggregation,Show the monthly average PM2.5 for Bhilwara in 2024 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Bhilwara') & (data['Timestamp'].dt.year == 2024)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Bhilwara 2024', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Bhilwara') & (data['Timestamp'].dt.year == 2024)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Bhilwara 2024', width=450, height=280)


</code>
"
2920,temporal_aggregation,Show the monthly average PM10 trend for Ajmer from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Ajmer'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Ajmer (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Ajmer'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Ajmer (2019–2024)', width=600, height=300)
     return chart


</code>
"
2921,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Jammu and Kashmir, Gujarat, and Telangana from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Jammu and Kashmir', 'Gujarat', 'Telangana']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Jammu and Kashmir vs Gujarat vs Telangana', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Jammu and Kashmir', 'Gujarat', 'Telangana']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Jammu and Kashmir vs Gujarat vs Telangana', width=550, height=320)
     return chart


</code>
"
2922,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Nagaland, Tamil Nadu, and West Bengal across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Nagaland', 'Tamil Nadu', 'West Bengal']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Nagaland', 'Tamil Nadu', 'West Bengal']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
2923,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Varanasi, Charkhi Dadri, and Palwal in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Varanasi', 'Charkhi Dadri', 'Palwal']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Varanasi vs Charkhi Dadri vs Palwal – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Varanasi', 'Charkhi Dadri', 'Palwal']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Varanasi vs Charkhi Dadri vs Palwal – 2022', width=550, height=320)
     return chart


</code>
"
2924,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Andhra Pradesh, Uttarakhand, and Madhya Pradesh in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Andhra Pradesh', 'Uttarakhand', 'Madhya Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Andhra Pradesh, Uttarakhand, UP – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Andhra Pradesh', 'Uttarakhand', 'Madhya Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Andhra Pradesh, Uttarakhand, UP – 2023', width=550, height=320)
     return chart


</code>
"
2925,temporal_aggregation,Show the monthly average PM2.5 for Dharwad in 2023 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Dharwad') & (data['Timestamp'].dt.year == 2023)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Dharwad 2023', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Dharwad') & (data['Timestamp'].dt.year == 2023)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Dharwad 2023', width=450, height=280)


</code>
"
2926,temporal_aggregation,Show the monthly average PM2.5 for Rupnagar in 2018 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Rupnagar') & (data['Timestamp'].dt.year == 2018)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Rupnagar 2018', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Rupnagar') & (data['Timestamp'].dt.year == 2018)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Rupnagar 2018', width=450, height=280)


</code>
"
2927,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Madhya Pradesh, West Bengal, and Rajasthan across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Madhya Pradesh', 'West Bengal', 'Rajasthan']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Madhya Pradesh', 'West Bengal', 'Rajasthan']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
2928,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Jammu and Kashmir, Uttarakhand, and Assam from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Jammu and Kashmir', 'Uttarakhand', 'Assam']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Jammu and Kashmir vs Uttarakhand vs Assam', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Jammu and Kashmir', 'Uttarakhand', 'Assam']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Jammu and Kashmir vs Uttarakhand vs Assam', width=550, height=320)
     return chart


</code>
"
2929,specific_pattern,Show a cumulative area chart of PM2.5 readings for Moradabad across 2022.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Moradabad') & (data['Timestamp'].dt.year == 2022)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Moradabad 2022', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Moradabad') & (data['Timestamp'].dt.year == 2022)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Moradabad 2022', width=600, height=300)
     return chart


</code>
"
2930,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Tamil Nadu, Uttarakhand, and Punjab in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Tamil Nadu', 'Uttarakhand', 'Punjab']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Tamil Nadu, Uttarakhand, UP – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Tamil Nadu', 'Uttarakhand', 'Punjab']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Tamil Nadu, Uttarakhand, UP – 2022', width=550, height=320)
     return chart


</code>
"
2931,temporal_aggregation,Show a monthly bar chart of the number of days West Bengal exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2024.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'West Bengal') & (data['Timestamp'].dt.year == 2024)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days West Bengal Exceeded WHO PM2.5 Guideline per Month – 2024', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'West Bengal') & (data['Timestamp'].dt.year == 2024)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days West Bengal Exceeded WHO PM2.5 Guideline per Month – 2024', width=500, height=300)
     return chart


</code>
"
2932,specific_pattern,Show a cumulative area chart of PM2.5 readings for Amravati across 2023.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Amravati') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Amravati 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Amravati') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Amravati 2023', width=600, height=300)
     return chart


</code>
"
2933,specific_pattern,Show a cumulative area chart of PM2.5 readings for Gurugram across 2021.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Gurugram') & (data['Timestamp'].dt.year == 2021)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Gurugram 2021', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Gurugram') & (data['Timestamp'].dt.year == 2021)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Gurugram 2021', width=600, height=300)
     return chart


</code>
"
2934,temporal_aggregation,Plot the weekly average PM2.5 for Jabalpur in 2023 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Jabalpur') & (data['Timestamp'].dt.year == 2023)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Jabalpur 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Jabalpur') & (data['Timestamp'].dt.year == 2023)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Jabalpur 2023', width=600, height=300)
     return chart


</code>
"
2935,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Arunachal Pradesh, Rajasthan, and Manipur across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Arunachal Pradesh', 'Rajasthan', 'Manipur']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Arunachal Pradesh', 'Rajasthan', 'Manipur']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
2936,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Assam, Puducherry, and Delhi from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Assam', 'Puducherry', 'Delhi']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Assam vs Puducherry vs Delhi', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Assam', 'Puducherry', 'Delhi']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Assam vs Puducherry vs Delhi', width=550, height=320)
     return chart


</code>
"
2937,specific_pattern,Show a cumulative area chart of PM2.5 readings for Barbil across 2024.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Barbil') & (data['Timestamp'].dt.year == 2024)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Barbil 2024', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Barbil') & (data['Timestamp'].dt.year == 2024)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Barbil 2024', width=600, height=300)
     return chart


</code>
"
2938,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Kerala, Haryana, and Uttar Pradesh in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Kerala', 'Haryana', 'Uttar Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Kerala, Haryana, UP – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Kerala', 'Haryana', 'Uttar Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Kerala, Haryana, UP – 2022', width=550, height=320)
     return chart


</code>
"
2939,temporal_aggregation,Show the monthly average PM10 trend for Eloor from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Eloor'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Eloor (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Eloor'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Eloor (2019–2024)', width=600, height=300)
     return chart


</code>
"
2940,temporal_aggregation,Show the monthly average PM10 trend for Belgaum from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Belgaum'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Belgaum (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Belgaum'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Belgaum (2019–2024)', width=600, height=300)
     return chart


</code>
"
2941,temporal_aggregation,Show the monthly average PM10 trend for Siliguri from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Siliguri'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Siliguri (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Siliguri'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Siliguri (2019–2024)', width=600, height=300)
     return chart


</code>
"
2942,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Gujarat, Karnataka, and Kerala in 2017 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Gujarat', 'Karnataka', 'Kerala']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2017)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Gujarat, Karnataka, UP – 2017', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Gujarat', 'Karnataka', 'Kerala']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2017)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Gujarat, Karnataka, UP – 2017', width=550, height=320)
     return chart


</code>
"
2943,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Chandigarh, Andhra Pradesh, and Jharkhand in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Chandigarh', 'Andhra Pradesh', 'Jharkhand']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Chandigarh, Andhra Pradesh, UP – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Chandigarh', 'Andhra Pradesh', 'Jharkhand']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Chandigarh, Andhra Pradesh, UP – 2023', width=550, height=320)
     return chart


</code>
"
2944,specific_pattern,Show a cumulative area chart of PM2.5 readings for Rupnagar across 2018.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Rupnagar') & (data['Timestamp'].dt.year == 2018)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Rupnagar 2018', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Rupnagar') & (data['Timestamp'].dt.year == 2018)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Rupnagar 2018', width=600, height=300)
     return chart


</code>
"
2945,specific_pattern,Show a cumulative area chart of PM2.5 readings for Muzaffarpur across 2022.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Muzaffarpur') & (data['Timestamp'].dt.year == 2022)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Muzaffarpur 2022', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Muzaffarpur') & (data['Timestamp'].dt.year == 2022)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Muzaffarpur 2022', width=600, height=300)
     return chart


</code>
"
2946,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Tripura, Mizoram, and Bihar from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Tripura', 'Mizoram', 'Bihar']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Tripura vs Mizoram vs Bihar', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Tripura', 'Mizoram', 'Bihar']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Tripura vs Mizoram vs Bihar', width=550, height=320)
     return chart


</code>
"
2947,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Bagalkot, Chikkamagaluru, and Kannur in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Bagalkot', 'Chikkamagaluru', 'Kannur']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Bagalkot vs Chikkamagaluru vs Kannur – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Bagalkot', 'Chikkamagaluru', 'Kannur']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Bagalkot vs Chikkamagaluru vs Kannur – 2024', width=550, height=320)
     return chart


</code>
"
2948,temporal_aggregation,Show the monthly average PM10 trend for Ulhasnagar from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Ulhasnagar'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Ulhasnagar (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Ulhasnagar'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Ulhasnagar (2019–2024)', width=600, height=300)
     return chart


</code>
"
2949,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Madhya Pradesh, Himachal Pradesh, and Andhra Pradesh across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Madhya Pradesh', 'Himachal Pradesh', 'Andhra Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Madhya Pradesh', 'Himachal Pradesh', 'Andhra Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
2950,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Puducherry, Odisha, and Rajasthan from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Puducherry', 'Odisha', 'Rajasthan']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Puducherry vs Odisha vs Rajasthan', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Puducherry', 'Odisha', 'Rajasthan']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Puducherry vs Odisha vs Rajasthan', width=550, height=320)
     return chart


</code>
"
2951,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Jharkhand, Madhya Pradesh, and Manipur across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Jharkhand', 'Madhya Pradesh', 'Manipur']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Jharkhand', 'Madhya Pradesh', 'Manipur']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
2952,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Jalgaon, Gwalior, and Bengaluru in 2019 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Jalgaon', 'Gwalior', 'Bengaluru']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Jalgaon vs Gwalior vs Bengaluru – 2019', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Jalgaon', 'Gwalior', 'Bengaluru']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Jalgaon vs Gwalior vs Bengaluru – 2019', width=550, height=320)
     return chart


</code>
"
2953,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Muzaffarpur, Kaithal, and Nagpur in 2017 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Muzaffarpur', 'Kaithal', 'Nagpur']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2017)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Muzaffarpur vs Kaithal vs Nagpur – 2017', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Muzaffarpur', 'Kaithal', 'Nagpur']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2017)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Muzaffarpur vs Kaithal vs Nagpur – 2017', width=550, height=320)
     return chart


</code>
"
2954,temporal_aggregation,Show the monthly average PM2.5 for Siwan in 2019 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Siwan') & (data['Timestamp'].dt.year == 2019)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Siwan 2019', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Siwan') & (data['Timestamp'].dt.year == 2019)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Siwan 2019', width=450, height=280)


</code>
"
2955,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Ooty, Rupnagar, and Visakhapatnam in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Ooty', 'Rupnagar', 'Visakhapatnam']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Ooty vs Rupnagar vs Visakhapatnam – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Ooty', 'Rupnagar', 'Visakhapatnam']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Ooty vs Rupnagar vs Visakhapatnam – 2023', width=550, height=320)
     return chart


</code>
"
2956,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Maharashtra, Maharashtra, and Tripura in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Maharashtra', 'Maharashtra', 'Tripura']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Maharashtra, Maharashtra, UP – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Maharashtra', 'Maharashtra', 'Tripura']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Maharashtra, Maharashtra, UP – 2023', width=550, height=320)
     return chart


</code>
"
2957,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Tripura, Delhi, and Telangana in 2018 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Tripura', 'Delhi', 'Telangana']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2018)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Tripura, Delhi, UP – 2018', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Tripura', 'Delhi', 'Telangana']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2018)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Tripura, Delhi, UP – 2018', width=550, height=320)
     return chart


</code>
"
2958,specific_pattern,Show a cumulative area chart of PM2.5 readings for Shivamogga across 2023.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Shivamogga') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Shivamogga 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Shivamogga') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Shivamogga 2023', width=600, height=300)
     return chart


</code>
"
2959,temporal_aggregation,Plot the weekly average PM2.5 for Chhapra in 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Chhapra') & (data['Timestamp'].dt.year == 2024)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Chhapra 2024', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Chhapra') & (data['Timestamp'].dt.year == 2024)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Chhapra 2024', width=600, height=300)
     return chart


</code>
"
2960,temporal_aggregation,Plot the weekly average PM2.5 for Panchkula in 2021 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Panchkula') & (data['Timestamp'].dt.year == 2021)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Panchkula 2021', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Panchkula') & (data['Timestamp'].dt.year == 2021)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Panchkula 2021', width=600, height=300)
     return chart


</code>
"
2961,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Jabalpur, Satna, and Rairangpur in 2018 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Jabalpur', 'Satna', 'Rairangpur']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2018)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Jabalpur vs Satna vs Rairangpur – 2018', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Jabalpur', 'Satna', 'Rairangpur']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2018)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Jabalpur vs Satna vs Rairangpur – 2018', width=550, height=320)
     return chart


</code>
"
2962,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Pratapgarh, Barrackpore, and Agartala in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Pratapgarh', 'Barrackpore', 'Agartala']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Pratapgarh vs Barrackpore vs Agartala – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Pratapgarh', 'Barrackpore', 'Agartala']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Pratapgarh vs Barrackpore vs Agartala – 2023', width=550, height=320)
     return chart


</code>
"
2963,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Sikkim, Chhattisgarh, and Tamil Nadu from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Sikkim', 'Chhattisgarh', 'Tamil Nadu']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Sikkim vs Chhattisgarh vs Tamil Nadu', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Sikkim', 'Chhattisgarh', 'Tamil Nadu']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Sikkim vs Chhattisgarh vs Tamil Nadu', width=550, height=320)
     return chart


</code>
"
2964,temporal_aggregation,Show the monthly average PM10 trend for Barbil from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Barbil'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Barbil (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Barbil'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Barbil (2019–2024)', width=600, height=300)
     return chart


</code>
"
2965,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Delhi, West Bengal, and Meghalaya across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Delhi', 'West Bengal', 'Meghalaya']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Delhi', 'West Bengal', 'Meghalaya']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
2966,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Bihar, Punjab, and Andhra Pradesh from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Bihar', 'Punjab', 'Andhra Pradesh']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Bihar vs Punjab vs Andhra Pradesh', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Bihar', 'Punjab', 'Andhra Pradesh']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Bihar vs Punjab vs Andhra Pradesh', width=550, height=320)
     return chart


</code>
"
2967,temporal_aggregation,Plot the weekly average PM2.5 for Dausa in 2023 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Dausa') & (data['Timestamp'].dt.year == 2023)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Dausa 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Dausa') & (data['Timestamp'].dt.year == 2023)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Dausa 2023', width=600, height=300)
     return chart


</code>
"
2968,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Uttarakhand, Maharashtra, and Chandigarh across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Uttarakhand', 'Maharashtra', 'Chandigarh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Uttarakhand', 'Maharashtra', 'Chandigarh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
2969,temporal_aggregation,Plot the weekly average PM2.5 for Munger in 2021 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Munger') & (data['Timestamp'].dt.year == 2021)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Munger 2021', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Munger') & (data['Timestamp'].dt.year == 2021)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Munger 2021', width=600, height=300)
     return chart


</code>
"
2970,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Tamil Nadu, Jharkhand, and Sikkim across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Tamil Nadu', 'Jharkhand', 'Sikkim']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Tamil Nadu', 'Jharkhand', 'Sikkim']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
2971,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Kerala, Jharkhand, and Chandigarh across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Kerala', 'Jharkhand', 'Chandigarh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Kerala', 'Jharkhand', 'Chandigarh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
2972,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Andhra Pradesh, Arunachal Pradesh, and Telangana across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Andhra Pradesh', 'Arunachal Pradesh', 'Telangana']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Andhra Pradesh', 'Arunachal Pradesh', 'Telangana']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
2973,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Delhi, Karnataka, and Tamil Nadu in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Delhi', 'Karnataka', 'Tamil Nadu']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Delhi, Karnataka, UP – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Delhi', 'Karnataka', 'Tamil Nadu']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Delhi, Karnataka, UP – 2023', width=550, height=320)
     return chart


</code>
"
2974,temporal_aggregation,Plot the weekly average PM2.5 for Siliguri in 2021 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Siliguri') & (data['Timestamp'].dt.year == 2021)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Siliguri 2021', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Siliguri') & (data['Timestamp'].dt.year == 2021)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Siliguri 2021', width=600, height=300)
     return chart


</code>
"
2975,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Delhi, Rajasthan, and Chandigarh across 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Delhi', 'Rajasthan', 'Chandigarh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Delhi', 'Rajasthan', 'Chandigarh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
     return chart


</code>
"
2976,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Mizoram, Uttar Pradesh, and Haryana from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Mizoram', 'Uttar Pradesh', 'Haryana']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Mizoram vs Uttar Pradesh vs Haryana', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Mizoram', 'Uttar Pradesh', 'Haryana']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Mizoram vs Uttar Pradesh vs Haryana', width=550, height=320)
     return chart


</code>
"
2977,temporal_aggregation,Show the monthly average PM10 trend for Davanagere from 2017 to 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Davanagere'].copy()
    df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Davanagere (2017–2022)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Davanagere'].copy()
     df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Davanagere (2017–2022)', width=600, height=300)
     return chart


</code>
"
2978,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Ernakulam, Belgaum, and Ahmedabad in 2017 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Ernakulam', 'Belgaum', 'Ahmedabad']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2017)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Ernakulam vs Belgaum vs Ahmedabad – 2017', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Ernakulam', 'Belgaum', 'Ahmedabad']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2017)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Ernakulam vs Belgaum vs Ahmedabad – 2017', width=550, height=320)
     return chart


</code>
"
2979,temporal_aggregation,Show the monthly average PM2.5 for Tonk in 2024 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Tonk') & (data['Timestamp'].dt.year == 2024)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Tonk 2024', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Tonk') & (data['Timestamp'].dt.year == 2024)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Tonk 2024', width=450, height=280)


</code>
"
2980,specific_pattern,Show a cumulative area chart of PM2.5 readings for Jhunjhunu across 2023.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Jhunjhunu') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Jhunjhunu 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Jhunjhunu') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Jhunjhunu 2023', width=600, height=300)
     return chart


</code>
"
2981,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Nagaland, Mizoram, and Haryana across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Nagaland', 'Mizoram', 'Haryana']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Nagaland', 'Mizoram', 'Haryana']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
2982,temporal_aggregation,Show the monthly average PM2.5 for Chamarajanagar in 2020 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Chamarajanagar') & (data['Timestamp'].dt.year == 2020)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Chamarajanagar 2020', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Chamarajanagar') & (data['Timestamp'].dt.year == 2020)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Chamarajanagar 2020', width=450, height=280)


</code>
"
2983,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Ahmedabad, Kanpur, and Pune in 2020 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Ahmedabad', 'Kanpur', 'Pune']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2020)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Ahmedabad vs Kanpur vs Pune – 2020', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Ahmedabad', 'Kanpur', 'Pune']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2020)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Ahmedabad vs Kanpur vs Pune – 2020', width=550, height=320)
     return chart


</code>
"
2984,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Manipur stations in 2024, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Manipur') & (data['Timestamp'].dt.year == 2024)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Manipur Stations 2024', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Manipur') & (data['Timestamp'].dt.year == 2024)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Manipur Stations 2024', width=450, height=350)


</code>
"
2985,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Begusarai, Jorapokhar, and Kolar in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Begusarai', 'Jorapokhar', 'Kolar']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Begusarai vs Jorapokhar vs Kolar – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Begusarai', 'Jorapokhar', 'Kolar']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Begusarai vs Jorapokhar vs Kolar – 2024', width=550, height=320)
     return chart


</code>
"
2986,temporal_aggregation,Show the monthly average PM2.5 for Tirupur in 2018 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Tirupur') & (data['Timestamp'].dt.year == 2018)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Tirupur 2018', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Tirupur') & (data['Timestamp'].dt.year == 2018)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Tirupur 2018', width=450, height=280)


</code>
"
2987,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Kerala stations in 2021, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Kerala') & (data['Timestamp'].dt.year == 2021)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Kerala Stations 2021', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Kerala') & (data['Timestamp'].dt.year == 2021)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Kerala Stations 2021', width=450, height=350)


</code>
"
2988,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Arunachal Pradesh, Delhi, and Uttar Pradesh in 2021 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Arunachal Pradesh', 'Delhi', 'Uttar Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Arunachal Pradesh, Delhi, UP – 2021', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Arunachal Pradesh', 'Delhi', 'Uttar Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Arunachal Pradesh, Delhi, UP – 2021', width=550, height=320)
     return chart


</code>
"
2989,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Telangana, Maharashtra, and Nagaland across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Telangana', 'Maharashtra', 'Nagaland']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Telangana', 'Maharashtra', 'Nagaland']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
2990,specific_pattern,Show a cumulative area chart of PM2.5 readings for Kohima across 2021.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Kohima') & (data['Timestamp'].dt.year == 2021)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Kohima 2021', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Kohima') & (data['Timestamp'].dt.year == 2021)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Kohima 2021', width=600, height=300)
     return chart


</code>
"
2991,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Mizoram, Puducherry, and Haryana from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Mizoram', 'Puducherry', 'Haryana']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Mizoram vs Puducherry vs Haryana', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Mizoram', 'Puducherry', 'Haryana']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Mizoram vs Puducherry vs Haryana', width=550, height=320)
     return chart


</code>
"
2992,temporal_aggregation,Show the monthly average PM10 trend for Agartala from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Agartala'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Agartala (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Agartala'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Agartala (2019–2024)', width=600, height=300)
     return chart


</code>
"
2993,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Bihar, Rajasthan, and Mizoram from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Bihar', 'Rajasthan', 'Mizoram']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Bihar vs Rajasthan vs Mizoram', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Bihar', 'Rajasthan', 'Mizoram']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Bihar vs Rajasthan vs Mizoram', width=550, height=320)
     return chart


</code>
"
2994,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Uttarakhand, Manipur, and Puducherry across 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Uttarakhand', 'Manipur', 'Puducherry']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Uttarakhand', 'Manipur', 'Puducherry']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
     return chart


</code>
"
2995,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Chhattisgarh, Kerala, and Uttar Pradesh from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Chhattisgarh', 'Kerala', 'Uttar Pradesh']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Chhattisgarh vs Kerala vs Uttar Pradesh', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Chhattisgarh', 'Kerala', 'Uttar Pradesh']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Chhattisgarh vs Kerala vs Uttar Pradesh', width=550, height=320)
     return chart


</code>
"
2996,temporal_aggregation,Show the monthly average PM2.5 for Agra in 2023 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Agra') & (data['Timestamp'].dt.year == 2023)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Agra 2023', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Agra') & (data['Timestamp'].dt.year == 2023)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Agra 2023', width=450, height=280)


</code>
"
2997,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Rajasthan, Madhya Pradesh, and Chandigarh in 2018 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Rajasthan', 'Madhya Pradesh', 'Chandigarh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2018)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Rajasthan, Madhya Pradesh, UP – 2018', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Rajasthan', 'Madhya Pradesh', 'Chandigarh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2018)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Rajasthan, Madhya Pradesh, UP – 2018', width=550, height=320)
     return chart


</code>
"
2998,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Odisha, Bihar, and Bihar from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Odisha', 'Bihar', 'Bihar']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Odisha vs Bihar vs Bihar', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Odisha', 'Bihar', 'Bihar']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Odisha vs Bihar vs Bihar', width=550, height=320)
     return chart


</code>
"
2999,specific_pattern,Plot the rolling 30-day average PM2.5 for Mizoram in 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Mizoram') & (data['Timestamp'].dt.year == 2024)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Mizoram 2024', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Mizoram') & (data['Timestamp'].dt.year == 2024)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Mizoram 2024', width=600, height=300)


</code>
"
3000,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Telangana, Tripura, and Haryana across 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Telangana', 'Tripura', 'Haryana']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Telangana', 'Tripura', 'Haryana']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
     return chart


</code>
"
3001,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Solapur, Pithampur, and Rajsamand in 2017 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Solapur', 'Pithampur', 'Rajsamand']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2017)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Solapur vs Pithampur vs Rajsamand – 2017', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Solapur', 'Pithampur', 'Rajsamand']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2017)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Solapur vs Pithampur vs Rajsamand – 2017', width=550, height=320)
     return chart


</code>
"
3002,temporal_aggregation,Show the monthly average PM2.5 for Tirunelveli in 2022 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Tirunelveli') & (data['Timestamp'].dt.year == 2022)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Tirunelveli 2022', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Tirunelveli') & (data['Timestamp'].dt.year == 2022)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Tirunelveli 2022', width=450, height=280)


</code>
"
3003,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Puducherry, Karnataka, and Bihar in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Puducherry', 'Karnataka', 'Bihar']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Puducherry, Karnataka, UP – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Puducherry', 'Karnataka', 'Bihar']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Puducherry, Karnataka, UP – 2022', width=550, height=320)
     return chart


</code>
"
3004,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Haryana, Punjab, and Uttar Pradesh from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Haryana', 'Punjab', 'Uttar Pradesh']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Haryana vs Punjab vs Uttar Pradesh', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Haryana', 'Punjab', 'Uttar Pradesh']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Haryana vs Punjab vs Uttar Pradesh', width=550, height=320)
     return chart


</code>
"
3005,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Nagaland, Madhya Pradesh, and Uttar Pradesh across 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Nagaland', 'Madhya Pradesh', 'Uttar Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Nagaland', 'Madhya Pradesh', 'Uttar Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
     return chart


</code>
"
3006,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Delhi, Jharkhand, and Jammu and Kashmir from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Delhi', 'Jharkhand', 'Jammu and Kashmir']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Delhi vs Jharkhand vs Jammu and Kashmir', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Delhi', 'Jharkhand', 'Jammu and Kashmir']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Delhi vs Jharkhand vs Jammu and Kashmir', width=550, height=320)
     return chart


</code>
"
3007,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Sikkim, Bihar, and Chhattisgarh in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Sikkim', 'Bihar', 'Chhattisgarh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Sikkim, Bihar, UP – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Sikkim', 'Bihar', 'Chhattisgarh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Sikkim, Bihar, UP – 2023', width=550, height=320)
     return chart


</code>
"
3008,specific_pattern,Plot the rolling 30-day average PM2.5 for Sikkim in 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Sikkim') & (data['Timestamp'].dt.year == 2024)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Sikkim 2024', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Sikkim') & (data['Timestamp'].dt.year == 2024)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Sikkim 2024', width=600, height=300)


</code>
"
3009,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Manipur, Punjab, and Haryana from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Manipur', 'Punjab', 'Haryana']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Manipur vs Punjab vs Haryana', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Manipur', 'Punjab', 'Haryana']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Manipur vs Punjab vs Haryana', width=550, height=320)
     return chart


</code>
"
3010,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Odisha, Jammu and Kashmir, and Tamil Nadu from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Odisha', 'Jammu and Kashmir', 'Tamil Nadu']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Odisha vs Jammu and Kashmir vs Tamil Nadu', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Odisha', 'Jammu and Kashmir', 'Tamil Nadu']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Odisha vs Jammu and Kashmir vs Tamil Nadu', width=550, height=320)
     return chart


</code>
"
3011,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Odisha, Tamil Nadu, and Himachal Pradesh from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Odisha', 'Tamil Nadu', 'Himachal Pradesh']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Odisha vs Tamil Nadu vs Himachal Pradesh', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Odisha', 'Tamil Nadu', 'Himachal Pradesh']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Odisha vs Tamil Nadu vs Himachal Pradesh', width=550, height=320)
     return chart


</code>
"
3012,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Haryana, Maharashtra, and Kerala across 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Haryana', 'Maharashtra', 'Kerala']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Haryana', 'Maharashtra', 'Kerala']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
     return chart


</code>
"
3013,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Telangana, Kerala, and Sikkim from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Telangana', 'Kerala', 'Sikkim']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Telangana vs Kerala vs Sikkim', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Telangana', 'Kerala', 'Sikkim']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Telangana vs Kerala vs Sikkim', width=550, height=320)
     return chart


</code>
"
3014,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Telangana, Assam, and Andhra Pradesh across 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Telangana', 'Assam', 'Andhra Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Telangana', 'Assam', 'Andhra Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
     return chart


</code>
"
3015,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for West Bengal, Nagaland, and Chandigarh across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['West Bengal', 'Nagaland', 'Chandigarh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['West Bengal', 'Nagaland', 'Chandigarh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
3016,temporal_aggregation,Plot the weekly average PM2.5 for Sonipat in 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Sonipat') & (data['Timestamp'].dt.year == 2024)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Sonipat 2024', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Sonipat') & (data['Timestamp'].dt.year == 2024)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Sonipat 2024', width=600, height=300)
     return chart


</code>
"
3017,temporal_aggregation,Plot the weekly average PM2.5 for Byrnihat in 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Byrnihat') & (data['Timestamp'].dt.year == 2024)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Byrnihat 2024', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Byrnihat') & (data['Timestamp'].dt.year == 2024)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Byrnihat 2024', width=600, height=300)
     return chart


</code>
"
3018,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Bihar, Karnataka, and Manipur from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Bihar', 'Karnataka', 'Manipur']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Bihar vs Karnataka vs Manipur', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Bihar', 'Karnataka', 'Manipur']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Bihar vs Karnataka vs Manipur', width=550, height=320)
     return chart


</code>
"
3019,temporal_aggregation,Show a monthly bar chart of the number of days Gujarat exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2024.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Gujarat') & (data['Timestamp'].dt.year == 2024)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Gujarat Exceeded WHO PM2.5 Guideline per Month – 2024', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Gujarat') & (data['Timestamp'].dt.year == 2024)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Gujarat Exceeded WHO PM2.5 Guideline per Month – 2024', width=500, height=300)
     return chart


</code>
"
3020,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Chhattisgarh, Assam, and Himachal Pradesh from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Chhattisgarh', 'Assam', 'Himachal Pradesh']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Chhattisgarh vs Assam vs Himachal Pradesh', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Chhattisgarh', 'Assam', 'Himachal Pradesh']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Chhattisgarh vs Assam vs Himachal Pradesh', width=550, height=320)
     return chart


</code>
"
3021,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Navi Mumbai, Purnia, and Jalna in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Navi Mumbai', 'Purnia', 'Jalna']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Navi Mumbai vs Purnia vs Jalna – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Navi Mumbai', 'Purnia', 'Jalna']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Navi Mumbai vs Purnia vs Jalna – 2022', width=550, height=320)
     return chart


</code>
"
3022,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Madhya Pradesh, Jammu and Kashmir, and Andhra Pradesh across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Madhya Pradesh', 'Jammu and Kashmir', 'Andhra Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Madhya Pradesh', 'Jammu and Kashmir', 'Andhra Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
3023,temporal_aggregation,Show the monthly average PM10 trend for Udaipur from 2017 to 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Udaipur'].copy()
    df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Udaipur (2017–2022)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Udaipur'].copy()
     df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Udaipur (2017–2022)', width=600, height=300)
     return chart


</code>
"
3024,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Uttarakhand, Sikkim, and Andhra Pradesh in 2018 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Uttarakhand', 'Sikkim', 'Andhra Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2018)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Uttarakhand, Sikkim, UP – 2018', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Uttarakhand', 'Sikkim', 'Andhra Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2018)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Uttarakhand, Sikkim, UP – 2018', width=550, height=320)
     return chart


</code>
"
3025,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Uttarakhand, Andhra Pradesh, and Rajasthan across 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Uttarakhand', 'Andhra Pradesh', 'Rajasthan']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Uttarakhand', 'Andhra Pradesh', 'Rajasthan']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
     return chart


</code>
"
3026,temporal_aggregation,Show the monthly average PM2.5 for Naharlagun in 2020 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Naharlagun') & (data['Timestamp'].dt.year == 2020)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Naharlagun 2020', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Naharlagun') & (data['Timestamp'].dt.year == 2020)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Naharlagun 2020', width=450, height=280)


</code>
"
3027,temporal_aggregation,Plot the weekly average PM2.5 for Rairangpur in 2023 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Rairangpur') & (data['Timestamp'].dt.year == 2023)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Rairangpur 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Rairangpur') & (data['Timestamp'].dt.year == 2023)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Rairangpur 2023', width=600, height=300)
     return chart


</code>
"
3028,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Uttar Pradesh, Maharashtra, and Kerala across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Uttar Pradesh', 'Maharashtra', 'Kerala']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Uttar Pradesh', 'Maharashtra', 'Kerala']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
3029,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Chandigarh, Tamil Nadu, and Tripura across 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Chandigarh', 'Tamil Nadu', 'Tripura']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Chandigarh', 'Tamil Nadu', 'Tripura']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
     return chart


</code>
"
3030,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Chandigarh, Sikkim, and Karnataka in 2021 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Chandigarh', 'Sikkim', 'Karnataka']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Chandigarh, Sikkim, UP – 2021', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Chandigarh', 'Sikkim', 'Karnataka']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Chandigarh, Sikkim, UP – 2021', width=550, height=320)
     return chart


</code>
"
3031,temporal_aggregation,Show a monthly bar chart of the number of days Chhattisgarh exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2023.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Chhattisgarh') & (data['Timestamp'].dt.year == 2023)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Chhattisgarh Exceeded WHO PM2.5 Guideline per Month – 2023', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Chhattisgarh') & (data['Timestamp'].dt.year == 2023)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Chhattisgarh Exceeded WHO PM2.5 Guideline per Month – 2023', width=500, height=300)
     return chart


</code>
"
3032,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Baghpat, Kochi, and Bhopal in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Baghpat', 'Kochi', 'Bhopal']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Baghpat vs Kochi vs Bhopal – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Baghpat', 'Kochi', 'Bhopal']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Baghpat vs Kochi vs Bhopal – 2024', width=550, height=320)
     return chart


</code>
"
3033,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Assam, Uttarakhand, and Haryana from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Assam', 'Uttarakhand', 'Haryana']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Assam vs Uttarakhand vs Haryana', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Assam', 'Uttarakhand', 'Haryana']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Assam vs Uttarakhand vs Haryana', width=550, height=320)
     return chart


</code>
"
3034,temporal_aggregation,Show the monthly average PM2.5 for Motihari in 2018 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Motihari') & (data['Timestamp'].dt.year == 2018)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Motihari 2018', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Motihari') & (data['Timestamp'].dt.year == 2018)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Motihari 2018', width=450, height=280)


</code>
"
3035,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Thiruvananthapuram, Tirupati, and Rourkela in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Thiruvananthapuram', 'Tirupati', 'Rourkela']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Thiruvananthapuram vs Tirupati vs Rourkela – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Thiruvananthapuram', 'Tirupati', 'Rourkela']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Thiruvananthapuram vs Tirupati vs Rourkela – 2023', width=550, height=320)
     return chart


</code>
"
3036,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Bihar, Delhi, and Manipur across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Bihar', 'Delhi', 'Manipur']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Bihar', 'Delhi', 'Manipur']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
3037,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Delhi, Himachal Pradesh, and Mizoram in 2017 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Delhi', 'Himachal Pradesh', 'Mizoram']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2017)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Delhi, Himachal Pradesh, UP – 2017', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Delhi', 'Himachal Pradesh', 'Mizoram']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2017)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Delhi, Himachal Pradesh, UP – 2017', width=550, height=320)
     return chart


</code>
"
3038,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Chandigarh, Sikkim, and Jammu and Kashmir across 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Chandigarh', 'Sikkim', 'Jammu and Kashmir']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Chandigarh', 'Sikkim', 'Jammu and Kashmir']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
     return chart


</code>
"
3039,temporal_aggregation,Show the monthly average PM2.5 for Belapur in 2018 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Belapur') & (data['Timestamp'].dt.year == 2018)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Belapur 2018', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Belapur') & (data['Timestamp'].dt.year == 2018)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Belapur 2018', width=450, height=280)


</code>
"
3040,temporal_aggregation,Show the monthly average PM2.5 for Palwal  in 2017 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Palwal ') & (data['Timestamp'].dt.year == 2017)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Palwal  2017', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Palwal ') & (data['Timestamp'].dt.year == 2017)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Palwal  2017', width=450, height=280)


</code>
"
3041,specific_pattern,Show a cumulative area chart of PM2.5 readings for Ambala across 2019.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Ambala') & (data['Timestamp'].dt.year == 2019)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Ambala 2019', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Ambala') & (data['Timestamp'].dt.year == 2019)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Ambala 2019', width=600, height=300)
     return chart


</code>
"
3042,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Delhi, Rajasthan, and Tripura across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Delhi', 'Rajasthan', 'Tripura']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Delhi', 'Rajasthan', 'Tripura']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
3043,temporal_aggregation,Show the monthly average PM10 trend for Chikkaballapur from 2017 to 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Chikkaballapur'].copy()
    df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Chikkaballapur (2017–2022)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Chikkaballapur'].copy()
     df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Chikkaballapur (2017–2022)', width=600, height=300)
     return chart


</code>
"
3044,temporal_aggregation,Show the monthly average PM2.5 for Dindigul in 2018 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Dindigul') & (data['Timestamp'].dt.year == 2018)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Dindigul 2018', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Dindigul') & (data['Timestamp'].dt.year == 2018)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Dindigul 2018', width=450, height=280)


</code>
"
3045,temporal_aggregation,Plot the weekly average PM2.5 for Fatehabad in 2019 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Fatehabad') & (data['Timestamp'].dt.year == 2019)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Fatehabad 2019', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Fatehabad') & (data['Timestamp'].dt.year == 2019)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Fatehabad 2019', width=600, height=300)
     return chart


</code>
"
3046,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Delhi, Chhattisgarh, and Haryana across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Delhi', 'Chhattisgarh', 'Haryana']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Delhi', 'Chhattisgarh', 'Haryana']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
3047,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Madhya Pradesh, Sikkim, and Odisha in 2021 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Madhya Pradesh', 'Sikkim', 'Odisha']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Madhya Pradesh, Sikkim, UP – 2021', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Madhya Pradesh', 'Sikkim', 'Odisha']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Madhya Pradesh, Sikkim, UP – 2021', width=550, height=320)
     return chart


</code>
"
3048,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Mizoram, Puducherry, and Meghalaya in 2019 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Mizoram', 'Puducherry', 'Meghalaya']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Mizoram, Puducherry, UP – 2019', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Mizoram', 'Puducherry', 'Meghalaya']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Mizoram, Puducherry, UP – 2019', width=550, height=320)
     return chart


</code>
"
3049,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Gujarat, Tripura, and Karnataka from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Gujarat', 'Tripura', 'Karnataka']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Gujarat vs Tripura vs Karnataka', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Gujarat', 'Tripura', 'Karnataka']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Gujarat vs Tripura vs Karnataka', width=550, height=320)
     return chart


</code>
"
3050,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Jammu and Kashmir, Meghalaya, and Delhi from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Jammu and Kashmir', 'Meghalaya', 'Delhi']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Jammu and Kashmir vs Meghalaya vs Delhi', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Jammu and Kashmir', 'Meghalaya', 'Delhi']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Jammu and Kashmir vs Meghalaya vs Delhi', width=550, height=320)
     return chart


</code>
"
3051,temporal_aggregation,Show the monthly average PM10 trend for Jalore from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Jalore'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Jalore (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Jalore'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Jalore (2019–2024)', width=600, height=300)
     return chart


</code>
"
3052,specific_pattern,Show a cumulative area chart of PM2.5 readings for Gaya across 2021.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Gaya') & (data['Timestamp'].dt.year == 2021)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Gaya 2021', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Gaya') & (data['Timestamp'].dt.year == 2021)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Gaya 2021', width=600, height=300)
     return chart


</code>
"
3053,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Puducherry, Andhra Pradesh, and Mizoram across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Puducherry', 'Andhra Pradesh', 'Mizoram']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Puducherry', 'Andhra Pradesh', 'Mizoram']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
3054,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Bihar stations in 2024, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Bihar') & (data['Timestamp'].dt.year == 2024)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Bihar Stations 2024', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Bihar') & (data['Timestamp'].dt.year == 2024)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Bihar Stations 2024', width=450, height=350)


</code>
"
3055,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Sikkim, Nagaland, and Uttar Pradesh in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Sikkim', 'Nagaland', 'Uttar Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Sikkim, Nagaland, UP – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Sikkim', 'Nagaland', 'Uttar Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Sikkim, Nagaland, UP – 2022', width=550, height=320)
     return chart


</code>
"
3056,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Puducherry, Manipur, and West Bengal from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Puducherry', 'Manipur', 'West Bengal']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Puducherry vs Manipur vs West Bengal', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Puducherry', 'Manipur', 'West Bengal']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Puducherry vs Manipur vs West Bengal', width=550, height=320)
     return chart


</code>
"
3057,temporal_aggregation,Show the monthly average PM2.5 for Sagar in 2023 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Sagar') & (data['Timestamp'].dt.year == 2023)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Sagar 2023', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Sagar') & (data['Timestamp'].dt.year == 2023)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Sagar 2023', width=450, height=280)


</code>
"
3058,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Tamil Nadu, Maharashtra, and Delhi in 2017 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Tamil Nadu', 'Maharashtra', 'Delhi']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2017)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Tamil Nadu, Maharashtra, UP – 2017', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Tamil Nadu', 'Maharashtra', 'Delhi']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2017)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Tamil Nadu, Maharashtra, UP – 2017', width=550, height=320)
     return chart


</code>
"
3059,temporal_aggregation,Show the monthly average PM2.5 for Kochi in 2022 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Kochi') & (data['Timestamp'].dt.year == 2022)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Kochi 2022', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Kochi') & (data['Timestamp'].dt.year == 2022)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Kochi 2022', width=450, height=280)


</code>
"
3060,specific_pattern,Show a cumulative area chart of PM2.5 readings for Manesar across 2019.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Manesar') & (data['Timestamp'].dt.year == 2019)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Manesar 2019', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Manesar') & (data['Timestamp'].dt.year == 2019)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Manesar 2019', width=600, height=300)
     return chart


</code>
"
3061,temporal_aggregation,Show the monthly average PM2.5 for Brajrajnagar in 2022 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Brajrajnagar') & (data['Timestamp'].dt.year == 2022)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Brajrajnagar 2022', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Brajrajnagar') & (data['Timestamp'].dt.year == 2022)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Brajrajnagar 2022', width=450, height=280)


</code>
"
3062,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Haryana, Nagaland, and Rajasthan in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Haryana', 'Nagaland', 'Rajasthan']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Haryana, Nagaland, UP – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Haryana', 'Nagaland', 'Rajasthan']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Haryana, Nagaland, UP – 2022', width=550, height=320)
     return chart


</code>
"
3063,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Gujarat, Delhi, and Haryana from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Gujarat', 'Delhi', 'Haryana']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Gujarat vs Delhi vs Haryana', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Gujarat', 'Delhi', 'Haryana']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Gujarat vs Delhi vs Haryana', width=550, height=320)
     return chart


</code>
"
3064,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Nagaland, Arunachal Pradesh, and Delhi across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Nagaland', 'Arunachal Pradesh', 'Delhi']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Nagaland', 'Arunachal Pradesh', 'Delhi']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
3065,temporal_aggregation,Show the monthly average PM2.5 for Sri Ganganagar in 2019 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Sri Ganganagar') & (data['Timestamp'].dt.year == 2019)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Sri Ganganagar 2019', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Sri Ganganagar') & (data['Timestamp'].dt.year == 2019)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Sri Ganganagar 2019', width=450, height=280)


</code>
"
3066,specific_pattern,Show a cumulative area chart of PM2.5 readings for Yamuna Nagar across 2021.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Yamuna Nagar') & (data['Timestamp'].dt.year == 2021)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Yamuna Nagar 2021', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Yamuna Nagar') & (data['Timestamp'].dt.year == 2021)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Yamuna Nagar 2021', width=600, height=300)
     return chart


</code>
"
3067,temporal_aggregation,Show the monthly average PM10 trend for Kohima from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Kohima'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Kohima (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Kohima'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Kohima (2019–2024)', width=600, height=300)
     return chart


</code>
"
3068,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Nagaland, Gujarat, and Assam across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Nagaland', 'Gujarat', 'Assam']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Nagaland', 'Gujarat', 'Assam']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
3069,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Jammu and Kashmir, Uttarakhand, and Delhi in 2017 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Jammu and Kashmir', 'Uttarakhand', 'Delhi']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2017)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Jammu and Kashmir, Uttarakhand, UP – 2017', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Jammu and Kashmir', 'Uttarakhand', 'Delhi']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2017)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Jammu and Kashmir, Uttarakhand, UP – 2017', width=550, height=320)
     return chart


</code>
"
3070,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Assam, Kerala, and Assam across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Assam', 'Kerala', 'Assam']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Assam', 'Kerala', 'Assam']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
3071,temporal_aggregation,Show the monthly average PM10 trend for Karwar from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Karwar'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Karwar (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Karwar'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Karwar (2019–2024)', width=600, height=300)
     return chart


</code>
"
3072,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Nagaland, Manipur, and Himachal Pradesh across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Nagaland', 'Manipur', 'Himachal Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Nagaland', 'Manipur', 'Himachal Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
3073,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Arunachal Pradesh, Jharkhand, and Andhra Pradesh from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Arunachal Pradesh', 'Jharkhand', 'Andhra Pradesh']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Arunachal Pradesh vs Jharkhand vs Andhra Pradesh', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Arunachal Pradesh', 'Jharkhand', 'Andhra Pradesh']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Arunachal Pradesh vs Jharkhand vs Andhra Pradesh', width=550, height=320)
     return chart


</code>
"
3074,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Andhra Pradesh, Karnataka, and Telangana from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Andhra Pradesh', 'Karnataka', 'Telangana']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Andhra Pradesh vs Karnataka vs Telangana', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Andhra Pradesh', 'Karnataka', 'Telangana']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Andhra Pradesh vs Karnataka vs Telangana', width=550, height=320)
     return chart


</code>
"
3075,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Bilaspur, Rajsamand, and Thane in 2017 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Bilaspur', 'Rajsamand', 'Thane']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2017)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Bilaspur vs Rajsamand vs Thane – 2017', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Bilaspur', 'Rajsamand', 'Thane']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2017)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Bilaspur vs Rajsamand vs Thane – 2017', width=550, height=320)
     return chart


</code>
"
3076,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Jharkhand, Tamil Nadu, and Uttar Pradesh from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Jharkhand', 'Tamil Nadu', 'Uttar Pradesh']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Jharkhand vs Tamil Nadu vs Uttar Pradesh', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Jharkhand', 'Tamil Nadu', 'Uttar Pradesh']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Jharkhand vs Tamil Nadu vs Uttar Pradesh', width=550, height=320)
     return chart


</code>
"
3077,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Andhra Pradesh, Puducherry, and Telangana in 2019 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Andhra Pradesh', 'Puducherry', 'Telangana']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Andhra Pradesh, Puducherry, UP – 2019', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Andhra Pradesh', 'Puducherry', 'Telangana']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Andhra Pradesh, Puducherry, UP – 2019', width=550, height=320)
     return chart


</code>
"
3078,temporal_aggregation,Show a monthly bar chart of the number of days Odisha exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2024.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Odisha') & (data['Timestamp'].dt.year == 2024)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Odisha Exceeded WHO PM2.5 Guideline per Month – 2024', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Odisha') & (data['Timestamp'].dt.year == 2024)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Odisha Exceeded WHO PM2.5 Guideline per Month – 2024', width=500, height=300)
     return chart


</code>
"
3079,temporal_aggregation,Show the monthly average PM10 trend for Raipur from 2017 to 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Raipur'].copy()
    df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Raipur (2017–2022)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Raipur'].copy()
     df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Raipur (2017–2022)', width=600, height=300)
     return chart


</code>
"
3080,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Andhra Pradesh, Gujarat, and Karnataka from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Andhra Pradesh', 'Gujarat', 'Karnataka']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Andhra Pradesh vs Gujarat vs Karnataka', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Andhra Pradesh', 'Gujarat', 'Karnataka']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Andhra Pradesh vs Gujarat vs Karnataka', width=550, height=320)
     return chart


</code>
"
3081,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Karnataka, Rajasthan, and Nagaland in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Karnataka', 'Rajasthan', 'Nagaland']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Karnataka, Rajasthan, UP – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Karnataka', 'Rajasthan', 'Nagaland']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Karnataka, Rajasthan, UP – 2024', width=550, height=320)
     return chart


</code>
"
3082,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Madhya Pradesh, Haryana, and Sikkim from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Madhya Pradesh', 'Haryana', 'Sikkim']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Madhya Pradesh vs Haryana vs Sikkim', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Madhya Pradesh', 'Haryana', 'Sikkim']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Madhya Pradesh vs Haryana vs Sikkim', width=550, height=320)
     return chart


</code>
"
3083,temporal_aggregation,Show the monthly average PM10 trend for Hassan from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Hassan'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Hassan (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Hassan'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Hassan (2019–2024)', width=600, height=300)
     return chart


</code>
"
3084,temporal_aggregation,Show the monthly average PM2.5 for Palkalaiperur in 2020 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Palkalaiperur') & (data['Timestamp'].dt.year == 2020)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Palkalaiperur 2020', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Palkalaiperur') & (data['Timestamp'].dt.year == 2020)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Palkalaiperur 2020', width=450, height=280)


</code>
"
3085,specific_pattern,Plot the rolling 30-day average PM2.5 for Gujarat in 2018 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Gujarat') & (data['Timestamp'].dt.year == 2018)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Gujarat 2018', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Gujarat') & (data['Timestamp'].dt.year == 2018)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Gujarat 2018', width=600, height=300)


</code>
"
3086,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Thane, Kolar, and Hajipur in 2018 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Thane', 'Kolar', 'Hajipur']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2018)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Thane vs Kolar vs Hajipur – 2018', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Thane', 'Kolar', 'Hajipur']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2018)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Thane vs Kolar vs Hajipur – 2018', width=550, height=320)
     return chart


</code>
"
3087,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Gujarat, Delhi, and Puducherry in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Gujarat', 'Delhi', 'Puducherry']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Gujarat, Delhi, UP – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Gujarat', 'Delhi', 'Puducherry']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Gujarat, Delhi, UP – 2024', width=550, height=320)
     return chart


</code>
"
3088,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Tripura, Jammu and Kashmir, and Assam from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Tripura', 'Jammu and Kashmir', 'Assam']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Tripura vs Jammu and Kashmir vs Assam', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Tripura', 'Jammu and Kashmir', 'Assam']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Tripura vs Jammu and Kashmir vs Assam', width=550, height=320)
     return chart


</code>
"
3089,temporal_aggregation,Show the monthly average PM10 trend for Hosur from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Hosur'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Hosur (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Hosur'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Hosur (2019–2024)', width=600, height=300)
     return chart


</code>
"
3090,specific_pattern,Show a cumulative area chart of PM2.5 readings for Bhiwani across 2022.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Bhiwani') & (data['Timestamp'].dt.year == 2022)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Bhiwani 2022', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Bhiwani') & (data['Timestamp'].dt.year == 2022)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Bhiwani 2022', width=600, height=300)
     return chart


</code>
"
3091,temporal_aggregation,Show the monthly average PM10 trend for Kolar from 2017 to 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Kolar'].copy()
    df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Kolar (2017–2022)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Kolar'].copy()
     df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Kolar (2017–2022)', width=600, height=300)
     return chart


</code>
"
3092,temporal_aggregation,Show the monthly average PM10 trend for Ariyalur from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Ariyalur'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Ariyalur (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Ariyalur'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Ariyalur (2019–2024)', width=600, height=300)
     return chart


</code>
"
3093,temporal_aggregation,Plot the weekly average PM2.5 for Hyderabad in 2017 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Hyderabad') & (data['Timestamp'].dt.year == 2017)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Hyderabad 2017', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Hyderabad') & (data['Timestamp'].dt.year == 2017)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Hyderabad 2017', width=600, height=300)
     return chart


</code>
"
3094,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Nagaland, Jharkhand, and Gujarat in 2018 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Nagaland', 'Jharkhand', 'Gujarat']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2018)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Nagaland, Jharkhand, UP – 2018', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Nagaland', 'Jharkhand', 'Gujarat']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2018)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Nagaland, Jharkhand, UP – 2018', width=550, height=320)
     return chart


</code>
"
3095,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Andhra Pradesh, Tamil Nadu, and Himachal Pradesh from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Andhra Pradesh', 'Tamil Nadu', 'Himachal Pradesh']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Andhra Pradesh vs Tamil Nadu vs Himachal Pradesh', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Andhra Pradesh', 'Tamil Nadu', 'Himachal Pradesh']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Andhra Pradesh vs Tamil Nadu vs Himachal Pradesh', width=550, height=320)
     return chart


</code>
"
3096,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Tamil Nadu, Andhra Pradesh, and Rajasthan in 2021 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Tamil Nadu', 'Andhra Pradesh', 'Rajasthan']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Tamil Nadu, Andhra Pradesh, UP – 2021', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Tamil Nadu', 'Andhra Pradesh', 'Rajasthan']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Tamil Nadu, Andhra Pradesh, UP – 2021', width=550, height=320)
     return chart


</code>
"
3097,temporal_aggregation,Show the monthly average PM2.5 for Bileipada in 2023 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Bileipada') & (data['Timestamp'].dt.year == 2023)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Bileipada 2023', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Bileipada') & (data['Timestamp'].dt.year == 2023)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Bileipada 2023', width=450, height=280)


</code>
"
3098,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Punjab, Jammu and Kashmir, and Kerala across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Punjab', 'Jammu and Kashmir', 'Kerala']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Punjab', 'Jammu and Kashmir', 'Kerala']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
3099,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Chandigarh stations in 2017, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Chandigarh') & (data['Timestamp'].dt.year == 2017)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Chandigarh Stations 2017', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Chandigarh') & (data['Timestamp'].dt.year == 2017)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Chandigarh Stations 2017', width=450, height=350)


</code>
"
3100,temporal_aggregation,Show the monthly average PM10 trend for Ghaziabad from 2017 to 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Ghaziabad'].copy()
    df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Ghaziabad (2017–2022)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Ghaziabad'].copy()
     df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Ghaziabad (2017–2022)', width=600, height=300)
     return chart


</code>
"
3101,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Uttarakhand stations in 2018, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Uttarakhand') & (data['Timestamp'].dt.year == 2018)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Uttarakhand Stations 2018', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Uttarakhand') & (data['Timestamp'].dt.year == 2018)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Uttarakhand Stations 2018', width=450, height=350)


</code>
"
3102,temporal_aggregation,Show the monthly average PM2.5 for Baddi in 2017 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Baddi') & (data['Timestamp'].dt.year == 2017)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Baddi 2017', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Baddi') & (data['Timestamp'].dt.year == 2017)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Baddi 2017', width=450, height=280)


</code>
"
3103,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Meghalaya, Sikkim, and Mizoram in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Meghalaya', 'Sikkim', 'Mizoram']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Meghalaya, Sikkim, UP – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Meghalaya', 'Sikkim', 'Mizoram']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Meghalaya, Sikkim, UP – 2022', width=550, height=320)
     return chart


</code>
"
3104,specific_pattern,Show a cumulative area chart of PM2.5 readings for Jalandhar across 2018.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Jalandhar') & (data['Timestamp'].dt.year == 2018)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Jalandhar 2018', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Jalandhar') & (data['Timestamp'].dt.year == 2018)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Jalandhar 2018', width=600, height=300)
     return chart


</code>
"
3105,specific_pattern,Show a cumulative area chart of PM2.5 readings for Belgaum across 2023.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Belgaum') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Belgaum 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Belgaum') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Belgaum 2023', width=600, height=300)
     return chart


</code>
"
3106,temporal_aggregation,Plot the weekly average PM2.5 for Jodhpur in 2018 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Jodhpur') & (data['Timestamp'].dt.year == 2018)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Jodhpur 2018', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Jodhpur') & (data['Timestamp'].dt.year == 2018)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Jodhpur 2018', width=600, height=300)
     return chart


</code>
"
3107,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Nagaland, Odisha, and Puducherry across 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Nagaland', 'Odisha', 'Puducherry']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Nagaland', 'Odisha', 'Puducherry']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
     return chart


</code>
"
3108,temporal_aggregation,Show the monthly average PM10 trend for Udaipur from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Udaipur'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Udaipur (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Udaipur'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Udaipur (2019–2024)', width=600, height=300)
     return chart


</code>
"
3109,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Greater Noida, Vijayawada, and Sirsa in 2017 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Greater Noida', 'Vijayawada', 'Sirsa']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2017)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Greater Noida vs Vijayawada vs Sirsa – 2017', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Greater Noida', 'Vijayawada', 'Sirsa']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2017)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Greater Noida vs Vijayawada vs Sirsa – 2017', width=550, height=320)
     return chart


</code>
"
3110,temporal_aggregation,Show a monthly bar chart of the number of days Kerala exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2023.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Kerala') & (data['Timestamp'].dt.year == 2023)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Kerala Exceeded WHO PM2.5 Guideline per Month – 2023', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Kerala') & (data['Timestamp'].dt.year == 2023)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Kerala Exceeded WHO PM2.5 Guideline per Month – 2023', width=500, height=300)
     return chart


</code>
"
3111,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Tripura, Uttar Pradesh, and Tamil Nadu across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Tripura', 'Uttar Pradesh', 'Tamil Nadu']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Tripura', 'Uttar Pradesh', 'Tamil Nadu']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
3112,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Tripura, Jharkhand, and Uttar Pradesh from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Tripura', 'Jharkhand', 'Uttar Pradesh']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Tripura vs Jharkhand vs Uttar Pradesh', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Tripura', 'Jharkhand', 'Uttar Pradesh']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Tripura vs Jharkhand vs Uttar Pradesh', width=550, height=320)
     return chart


</code>
"
3113,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for West Bengal, Uttarakhand, and Tamil Nadu from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['West Bengal', 'Uttarakhand', 'Tamil Nadu']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: West Bengal vs Uttarakhand vs Tamil Nadu', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['West Bengal', 'Uttarakhand', 'Tamil Nadu']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: West Bengal vs Uttarakhand vs Tamil Nadu', width=550, height=320)
     return chart


</code>
"
3114,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Telangana, West Bengal, and Jharkhand in 2021 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Telangana', 'West Bengal', 'Jharkhand']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Telangana, West Bengal, UP – 2021', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Telangana', 'West Bengal', 'Jharkhand']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Telangana, West Bengal, UP – 2021', width=550, height=320)
     return chart


</code>
"
3115,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Khurja, Tensa, and Rajamahendravaram in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Khurja', 'Tensa', 'Rajamahendravaram']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Khurja vs Tensa vs Rajamahendravaram – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Khurja', 'Tensa', 'Rajamahendravaram']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Khurja vs Tensa vs Rajamahendravaram – 2022', width=550, height=320)
     return chart


</code>
"
3116,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Chandigarh stations in 2021, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Chandigarh') & (data['Timestamp'].dt.year == 2021)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Chandigarh Stations 2021', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Chandigarh') & (data['Timestamp'].dt.year == 2021)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Chandigarh Stations 2021', width=450, height=350)


</code>
"
3117,specific_pattern,Plot the rolling 30-day average PM2.5 for Tripura in 2017 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Tripura') & (data['Timestamp'].dt.year == 2017)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Tripura 2017', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Tripura') & (data['Timestamp'].dt.year == 2017)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Tripura 2017', width=600, height=300)


</code>
"
3118,temporal_aggregation,Plot the weekly average PM2.5 for Kalyan in 2019 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Kalyan') & (data['Timestamp'].dt.year == 2019)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Kalyan 2019', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Kalyan') & (data['Timestamp'].dt.year == 2019)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Kalyan 2019', width=600, height=300)
     return chart


</code>
"
3119,temporal_aggregation,Show the monthly average PM2.5 for Sivasagar in 2019 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Sivasagar') & (data['Timestamp'].dt.year == 2019)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Sivasagar 2019', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Sivasagar') & (data['Timestamp'].dt.year == 2019)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Sivasagar 2019', width=450, height=280)


</code>
"
3120,specific_pattern,Show a cumulative area chart of PM2.5 readings for Ahmednagar across 2024.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Ahmednagar') & (data['Timestamp'].dt.year == 2024)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Ahmednagar 2024', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Ahmednagar') & (data['Timestamp'].dt.year == 2024)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Ahmednagar 2024', width=600, height=300)
     return chart


</code>
"
3121,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Odisha, Madhya Pradesh, and Rajasthan in 2018 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Odisha', 'Madhya Pradesh', 'Rajasthan']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2018)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Odisha, Madhya Pradesh, UP – 2018', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Odisha', 'Madhya Pradesh', 'Rajasthan']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2018)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Odisha, Madhya Pradesh, UP – 2018', width=550, height=320)
     return chart


</code>
"
3122,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Gujarat stations in 2023, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Gujarat') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Gujarat Stations 2023', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Gujarat') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Gujarat Stations 2023', width=450, height=350)


</code>
"
3123,temporal_aggregation,Show the monthly average PM2.5 for Bundi in 2017 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Bundi') & (data['Timestamp'].dt.year == 2017)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Bundi 2017', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Bundi') & (data['Timestamp'].dt.year == 2017)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Bundi 2017', width=450, height=280)


</code>
"
3124,specific_pattern,Plot the rolling 30-day average PM2.5 for Himachal Pradesh in 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Himachal Pradesh') & (data['Timestamp'].dt.year == 2022)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Himachal Pradesh 2022', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Himachal Pradesh') & (data['Timestamp'].dt.year == 2022)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Himachal Pradesh 2022', width=600, height=300)


</code>
"
3125,temporal_aggregation,Show the monthly average PM10 trend for Jhansi from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Jhansi'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Jhansi (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Jhansi'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Jhansi (2019–2024)', width=600, height=300)
     return chart


</code>
"
3126,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Chhapra, Raichur, and Katihar in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Chhapra', 'Raichur', 'Katihar']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Chhapra vs Raichur vs Katihar – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Chhapra', 'Raichur', 'Katihar']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Chhapra vs Raichur vs Katihar – 2022', width=550, height=320)
     return chart


</code>
"
3127,temporal_aggregation,Show the monthly average PM2.5 for Jorapokhar in 2018 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Jorapokhar') & (data['Timestamp'].dt.year == 2018)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Jorapokhar 2018', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Jorapokhar') & (data['Timestamp'].dt.year == 2018)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Jorapokhar 2018', width=450, height=280)


</code>
"
3128,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Karnataka, Jharkhand, and Uttar Pradesh in 2017 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Karnataka', 'Jharkhand', 'Uttar Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2017)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Karnataka, Jharkhand, UP – 2017', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Karnataka', 'Jharkhand', 'Uttar Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2017)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Karnataka, Jharkhand, UP – 2017', width=550, height=320)
     return chart


</code>
"
3129,temporal_aggregation,Plot the weekly average PM2.5 for Koppal in 2020 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Koppal') & (data['Timestamp'].dt.year == 2020)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Koppal 2020', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Koppal') & (data['Timestamp'].dt.year == 2020)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Koppal 2020', width=600, height=300)
     return chart


</code>
"
3130,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Bihar, Manipur, and Jharkhand across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Bihar', 'Manipur', 'Jharkhand']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Bihar', 'Manipur', 'Jharkhand']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
3131,temporal_aggregation,Show a monthly bar chart of the number of days Assam exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2021.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Assam') & (data['Timestamp'].dt.year == 2021)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Assam Exceeded WHO PM2.5 Guideline per Month – 2021', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Assam') & (data['Timestamp'].dt.year == 2021)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Assam Exceeded WHO PM2.5 Guideline per Month – 2021', width=500, height=300)
     return chart


</code>
"
3132,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Manguraha, Bathinda, and Ujjain in 2019 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Manguraha', 'Bathinda', 'Ujjain']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Manguraha vs Bathinda vs Ujjain – 2019', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Manguraha', 'Bathinda', 'Ujjain']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Manguraha vs Bathinda vs Ujjain – 2019', width=550, height=320)
     return chart


</code>
"
3133,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Madhya Pradesh, Chandigarh, and Punjab across 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Madhya Pradesh', 'Chandigarh', 'Punjab']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Madhya Pradesh', 'Chandigarh', 'Punjab']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
     return chart


</code>
"
3134,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Meghalaya, Nagaland, and Nagaland across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Meghalaya', 'Nagaland', 'Nagaland']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Meghalaya', 'Nagaland', 'Nagaland']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
3135,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Haryana, Assam, and Jammu and Kashmir across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Haryana', 'Assam', 'Jammu and Kashmir']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Haryana', 'Assam', 'Jammu and Kashmir']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
3136,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Meghalaya, Tamil Nadu, and Madhya Pradesh in 2017 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Meghalaya', 'Tamil Nadu', 'Madhya Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2017)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Meghalaya, Tamil Nadu, UP – 2017', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Meghalaya', 'Tamil Nadu', 'Madhya Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2017)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Meghalaya, Tamil Nadu, UP – 2017', width=550, height=320)
     return chart


</code>
"
3137,temporal_aggregation,Show the monthly average PM2.5 for Udaipur in 2017 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Udaipur') & (data['Timestamp'].dt.year == 2017)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Udaipur 2017', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Udaipur') & (data['Timestamp'].dt.year == 2017)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Udaipur 2017', width=450, height=280)


</code>
"
3138,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Meghalaya, Punjab, and Tripura from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Meghalaya', 'Punjab', 'Tripura']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Meghalaya vs Punjab vs Tripura', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Meghalaya', 'Punjab', 'Tripura']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Meghalaya vs Punjab vs Tripura', width=550, height=320)
     return chart


</code>
"
3139,temporal_aggregation,Show the monthly average PM10 trend for Chengalpattu from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Chengalpattu'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Chengalpattu (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Chengalpattu'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Chengalpattu (2019–2024)', width=600, height=300)
     return chart


</code>
"
3140,specific_pattern,Show a cumulative area chart of PM2.5 readings for Sikar across 2023.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Sikar') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Sikar 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Sikar') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Sikar 2023', width=600, height=300)
     return chart


</code>
"
3141,temporal_aggregation,Show the monthly average PM2.5 for Baripada in 2017 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Baripada') & (data['Timestamp'].dt.year == 2017)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Baripada 2017', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Baripada') & (data['Timestamp'].dt.year == 2017)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Baripada 2017', width=450, height=280)


</code>
"
3142,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Puducherry, Odisha, and Mizoram across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Puducherry', 'Odisha', 'Mizoram']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Puducherry', 'Odisha', 'Mizoram']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
3143,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Sikkim, Himachal Pradesh, and Haryana in 2018 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Sikkim', 'Himachal Pradesh', 'Haryana']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2018)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Sikkim, Himachal Pradesh, UP – 2018', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Sikkim', 'Himachal Pradesh', 'Haryana']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2018)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Sikkim, Himachal Pradesh, UP – 2018', width=550, height=320)
     return chart


</code>
"
3144,temporal_aggregation,Show the monthly average PM10 trend for Muzaffarnagar from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Muzaffarnagar'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Muzaffarnagar (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Muzaffarnagar'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Muzaffarnagar (2019–2024)', width=600, height=300)
     return chart


</code>
"
3145,temporal_aggregation,Show the monthly average PM2.5 for Rourkela in 2024 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Rourkela') & (data['Timestamp'].dt.year == 2024)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Rourkela 2024', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Rourkela') & (data['Timestamp'].dt.year == 2024)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Rourkela 2024', width=450, height=280)


</code>
"
3146,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Andhra Pradesh, Andhra Pradesh, and Madhya Pradesh in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Andhra Pradesh', 'Andhra Pradesh', 'Madhya Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Andhra Pradesh, Andhra Pradesh, UP – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Andhra Pradesh', 'Andhra Pradesh', 'Madhya Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Andhra Pradesh, Andhra Pradesh, UP – 2022', width=550, height=320)
     return chart


</code>
"
3147,temporal_aggregation,Plot the weekly average PM2.5 for Sirsa in 2019 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Sirsa') & (data['Timestamp'].dt.year == 2019)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Sirsa 2019', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Sirsa') & (data['Timestamp'].dt.year == 2019)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Sirsa 2019', width=600, height=300)
     return chart


</code>
"
3148,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Patiala, Suakati, and Jalandhar in 2020 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Patiala', 'Suakati', 'Jalandhar']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2020)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Patiala vs Suakati vs Jalandhar – 2020', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Patiala', 'Suakati', 'Jalandhar']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2020)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Patiala vs Suakati vs Jalandhar – 2020', width=550, height=320)
     return chart


</code>
"
3149,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Andhra Pradesh, Chandigarh, and Uttar Pradesh in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Andhra Pradesh', 'Chandigarh', 'Uttar Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Andhra Pradesh, Chandigarh, UP – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Andhra Pradesh', 'Chandigarh', 'Uttar Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Andhra Pradesh, Chandigarh, UP – 2024', width=550, height=320)
     return chart


</code>
"
3150,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Chandigarh, Manipur, and Sikkim across 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Chandigarh', 'Manipur', 'Sikkim']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Chandigarh', 'Manipur', 'Sikkim']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
     return chart


</code>
"
3151,temporal_aggregation,Show the monthly average PM10 trend for Agra from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Agra'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Agra (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Agra'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Agra (2019–2024)', width=600, height=300)
     return chart


</code>
"
3152,specific_pattern,Show a cumulative area chart of PM2.5 readings for Chittorgarh across 2023.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Chittorgarh') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Chittorgarh 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Chittorgarh') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Chittorgarh 2023', width=600, height=300)
     return chart


</code>
"
3153,temporal_aggregation,Plot the weekly average PM2.5 for Churu in 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Churu') & (data['Timestamp'].dt.year == 2024)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Churu 2024', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Churu') & (data['Timestamp'].dt.year == 2024)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Churu 2024', width=600, height=300)
     return chart


</code>
"
3154,temporal_aggregation,Show the monthly average PM2.5 for Barbil in 2023 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Barbil') & (data['Timestamp'].dt.year == 2023)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Barbil 2023', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Barbil') & (data['Timestamp'].dt.year == 2023)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Barbil 2023', width=450, height=280)


</code>
"
3155,specific_pattern,Show a cumulative area chart of PM2.5 readings for Ghaziabad across 2023.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Ghaziabad') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Ghaziabad 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Ghaziabad') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Ghaziabad 2023', width=600, height=300)
     return chart


</code>
"
3156,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Kerala, Tripura, and Tripura in 2019 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Kerala', 'Tripura', 'Tripura']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Kerala, Tripura, UP – 2019', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Kerala', 'Tripura', 'Tripura']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Kerala, Tripura, UP – 2019', width=550, height=320)
     return chart


</code>
"
3157,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Madhya Pradesh, Meghalaya, and Manipur in 2017 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Madhya Pradesh', 'Meghalaya', 'Manipur']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2017)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Madhya Pradesh, Meghalaya, UP – 2017', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Madhya Pradesh', 'Meghalaya', 'Manipur']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2017)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Madhya Pradesh, Meghalaya, UP – 2017', width=550, height=320)
     return chart


</code>
"
3158,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Jammu and Kashmir, West Bengal, and Punjab from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Jammu and Kashmir', 'West Bengal', 'Punjab']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Jammu and Kashmir vs West Bengal vs Punjab', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Jammu and Kashmir', 'West Bengal', 'Punjab']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Jammu and Kashmir vs West Bengal vs Punjab', width=550, height=320)
     return chart


</code>
"
3159,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Arunachal Pradesh, Haryana, and Uttarakhand from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Arunachal Pradesh', 'Haryana', 'Uttarakhand']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Arunachal Pradesh vs Haryana vs Uttarakhand', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Arunachal Pradesh', 'Haryana', 'Uttarakhand']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Arunachal Pradesh vs Haryana vs Uttarakhand', width=550, height=320)
     return chart


</code>
"
3160,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Odisha, Kerala, and Madhya Pradesh from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Odisha', 'Kerala', 'Madhya Pradesh']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Odisha vs Kerala vs Madhya Pradesh', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Odisha', 'Kerala', 'Madhya Pradesh']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Odisha vs Kerala vs Madhya Pradesh', width=550, height=320)
     return chart


</code>
"
3161,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Gujarat, Kerala, and Sikkim across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Gujarat', 'Kerala', 'Sikkim']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Gujarat', 'Kerala', 'Sikkim']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
3162,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Assam, Tripura, and Telangana from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Assam', 'Tripura', 'Telangana']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Assam vs Tripura vs Telangana', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Assam', 'Tripura', 'Telangana']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Assam vs Tripura vs Telangana', width=550, height=320)
     return chart


</code>
"
3163,temporal_aggregation,Plot the weekly average PM2.5 for Jalgaon in 2023 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Jalgaon') & (data['Timestamp'].dt.year == 2023)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Jalgaon 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Jalgaon') & (data['Timestamp'].dt.year == 2023)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Jalgaon 2023', width=600, height=300)
     return chart


</code>
"
3164,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Uttarakhand, Nagaland, and Delhi across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Uttarakhand', 'Nagaland', 'Delhi']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Uttarakhand', 'Nagaland', 'Delhi']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
3165,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Rajasthan, Rajasthan, and Kerala across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Rajasthan', 'Rajasthan', 'Kerala']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Rajasthan', 'Rajasthan', 'Kerala']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
3166,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Manipur, Meghalaya, and Uttar Pradesh in 2021 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Manipur', 'Meghalaya', 'Uttar Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Manipur, Meghalaya, UP – 2021', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Manipur', 'Meghalaya', 'Uttar Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Manipur, Meghalaya, UP – 2021', width=550, height=320)
     return chart


</code>
"
3167,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Kerala, Manipur, and Madhya Pradesh across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Kerala', 'Manipur', 'Madhya Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Kerala', 'Manipur', 'Madhya Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
3168,specific_pattern,Show a cumulative area chart of PM2.5 readings for Ajmer across 2022.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Ajmer') & (data['Timestamp'].dt.year == 2022)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Ajmer 2022', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Ajmer') & (data['Timestamp'].dt.year == 2022)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Ajmer 2022', width=600, height=300)
     return chart


</code>
"
3169,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Mizoram, Jammu and Kashmir, and Meghalaya in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Mizoram', 'Jammu and Kashmir', 'Meghalaya']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Mizoram, Jammu and Kashmir, UP – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Mizoram', 'Jammu and Kashmir', 'Meghalaya']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Mizoram, Jammu and Kashmir, UP – 2024', width=550, height=320)
     return chart


</code>
"
3170,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Nagaland, Maharashtra, and Telangana across 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Nagaland', 'Maharashtra', 'Telangana']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Nagaland', 'Maharashtra', 'Telangana']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
     return chart


</code>
"
3171,temporal_aggregation,Show the monthly average PM10 trend for Ooty from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Ooty'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Ooty (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Ooty'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Ooty (2019–2024)', width=600, height=300)
     return chart


</code>
"
3172,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Ramanagara, Tiruchirappalli, and Eloor in 2019 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Ramanagara', 'Tiruchirappalli', 'Eloor']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Ramanagara vs Tiruchirappalli vs Eloor – 2019', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Ramanagara', 'Tiruchirappalli', 'Eloor']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Ramanagara vs Tiruchirappalli vs Eloor – 2019', width=550, height=320)
     return chart


</code>
"
3173,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Odisha, Sikkim, and Andhra Pradesh from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Odisha', 'Sikkim', 'Andhra Pradesh']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Odisha vs Sikkim vs Andhra Pradesh', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Odisha', 'Sikkim', 'Andhra Pradesh']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Odisha vs Sikkim vs Andhra Pradesh', width=550, height=320)
     return chart


</code>
"
3174,temporal_aggregation,Show a monthly bar chart of the number of days Sikkim exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2022.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Sikkim') & (data['Timestamp'].dt.year == 2022)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Sikkim Exceeded WHO PM2.5 Guideline per Month – 2022', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Sikkim') & (data['Timestamp'].dt.year == 2022)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Sikkim Exceeded WHO PM2.5 Guideline per Month – 2022', width=500, height=300)
     return chart


</code>
"
3175,temporal_aggregation,Show the monthly average PM2.5 for Gwalior in 2020 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Gwalior') & (data['Timestamp'].dt.year == 2020)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Gwalior 2020', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Gwalior') & (data['Timestamp'].dt.year == 2020)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Gwalior 2020', width=450, height=280)


</code>
"
3176,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Haryana, Tamil Nadu, and Punjab across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Haryana', 'Tamil Nadu', 'Punjab']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Haryana', 'Tamil Nadu', 'Punjab']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
3177,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Jharkhand, Tripura, and Uttarakhand across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Jharkhand', 'Tripura', 'Uttarakhand']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Jharkhand', 'Tripura', 'Uttarakhand']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
3178,temporal_aggregation,Show the monthly average PM2.5 for Kishanganj in 2024 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Kishanganj') & (data['Timestamp'].dt.year == 2024)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Kishanganj 2024', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Kishanganj') & (data['Timestamp'].dt.year == 2024)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Kishanganj 2024', width=450, height=280)


</code>
"
3179,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Puducherry, Uttarakhand, and West Bengal from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Puducherry', 'Uttarakhand', 'West Bengal']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Puducherry vs Uttarakhand vs West Bengal', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Puducherry', 'Uttarakhand', 'West Bengal']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Puducherry vs Uttarakhand vs West Bengal', width=550, height=320)
     return chart


</code>
"
3180,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Tamil Nadu stations in 2024, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Tamil Nadu') & (data['Timestamp'].dt.year == 2024)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Tamil Nadu Stations 2024', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Tamil Nadu') & (data['Timestamp'].dt.year == 2024)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Tamil Nadu Stations 2024', width=450, height=350)


</code>
"
3181,temporal_aggregation,Plot the weekly average PM2.5 for Jhalawar in 2023 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Jhalawar') & (data['Timestamp'].dt.year == 2023)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Jhalawar 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Jhalawar') & (data['Timestamp'].dt.year == 2023)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Jhalawar 2023', width=600, height=300)
     return chart


</code>
"
3182,temporal_aggregation,Show the monthly average PM2.5 for Baghpat in 2022 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Baghpat') & (data['Timestamp'].dt.year == 2022)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Baghpat 2022', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Baghpat') & (data['Timestamp'].dt.year == 2022)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Baghpat 2022', width=450, height=280)


</code>
"
3183,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Uttarakhand, Maharashtra, and Rajasthan from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Uttarakhand', 'Maharashtra', 'Rajasthan']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Uttarakhand vs Maharashtra vs Rajasthan', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Uttarakhand', 'Maharashtra', 'Rajasthan']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Uttarakhand vs Maharashtra vs Rajasthan', width=550, height=320)
     return chart


</code>
"
3184,specific_pattern,Show a cumulative area chart of PM2.5 readings for Ahmedabad across 2023.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Ahmedabad') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Ahmedabad 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Ahmedabad') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Ahmedabad 2023', width=600, height=300)
     return chart


</code>
"
3185,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Uttar Pradesh, Chandigarh, and Assam from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Uttar Pradesh', 'Chandigarh', 'Assam']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Uttar Pradesh vs Chandigarh vs Assam', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Uttar Pradesh', 'Chandigarh', 'Assam']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Uttar Pradesh vs Chandigarh vs Assam', width=550, height=320)
     return chart


</code>
"
3186,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Sirsa, Hisar, and Imphal in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Sirsa', 'Hisar', 'Imphal']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Sirsa vs Hisar vs Imphal – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Sirsa', 'Hisar', 'Imphal']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Sirsa vs Hisar vs Imphal – 2024', width=550, height=320)
     return chart


</code>
"
3187,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Mizoram, Jharkhand, and Andhra Pradesh in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Mizoram', 'Jharkhand', 'Andhra Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Mizoram, Jharkhand, UP – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Mizoram', 'Jharkhand', 'Andhra Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Mizoram, Jharkhand, UP – 2023', width=550, height=320)
     return chart


</code>
"
3188,specific_pattern,Plot the rolling 30-day average PM2.5 for Uttar Pradesh in 2020 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Uttar Pradesh') & (data['Timestamp'].dt.year == 2020)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Uttar Pradesh 2020', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Uttar Pradesh') & (data['Timestamp'].dt.year == 2020)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Uttar Pradesh 2020', width=600, height=300)


</code>
"
3189,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Tamil Nadu, Telangana, and Nagaland across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Tamil Nadu', 'Telangana', 'Nagaland']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Tamil Nadu', 'Telangana', 'Nagaland']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
3190,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Delhi stations in 2020, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Delhi') & (data['Timestamp'].dt.year == 2020)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Delhi Stations 2020', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Delhi') & (data['Timestamp'].dt.year == 2020)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Delhi Stations 2020', width=450, height=350)


</code>
"
3191,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Karnataka, Haryana, and Bihar in 2021 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Karnataka', 'Haryana', 'Bihar']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Karnataka, Haryana, UP – 2021', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Karnataka', 'Haryana', 'Bihar']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Karnataka, Haryana, UP – 2021', width=550, height=320)
     return chart


</code>
"
3192,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Sikkim, Haryana, and Kerala across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Sikkim', 'Haryana', 'Kerala']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Sikkim', 'Haryana', 'Kerala']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
3193,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Manipur, Bihar, and Haryana across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Manipur', 'Bihar', 'Haryana']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Manipur', 'Bihar', 'Haryana']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
3194,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Assam, Telangana, and Nagaland across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Assam', 'Telangana', 'Nagaland']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Assam', 'Telangana', 'Nagaland']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
3195,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Andhra Pradesh, Tripura, and Tamil Nadu across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Andhra Pradesh', 'Tripura', 'Tamil Nadu']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Andhra Pradesh', 'Tripura', 'Tamil Nadu']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
3196,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Jammu and Kashmir, Nagaland, and West Bengal in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Jammu and Kashmir', 'Nagaland', 'West Bengal']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Jammu and Kashmir, Nagaland, UP – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Jammu and Kashmir', 'Nagaland', 'West Bengal']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Jammu and Kashmir, Nagaland, UP – 2022', width=550, height=320)
     return chart


</code>
"
3197,temporal_aggregation,Show the monthly average PM10 trend for Rajamahendravaram from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Rajamahendravaram'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Rajamahendravaram (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Rajamahendravaram'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Rajamahendravaram (2019–2024)', width=600, height=300)
     return chart


</code>
"
3198,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Puducherry, Bihar, and Tamil Nadu from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Puducherry', 'Bihar', 'Tamil Nadu']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Puducherry vs Bihar vs Tamil Nadu', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Puducherry', 'Bihar', 'Tamil Nadu']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Puducherry vs Bihar vs Tamil Nadu', width=550, height=320)
     return chart


</code>
"
3199,temporal_aggregation,Plot the weekly average PM2.5 for Hubballi in 2019 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Hubballi') & (data['Timestamp'].dt.year == 2019)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Hubballi 2019', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Hubballi') & (data['Timestamp'].dt.year == 2019)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Hubballi 2019', width=600, height=300)
     return chart


</code>
"
3200,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Haryana, Gujarat, and Chandigarh in 2017 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Haryana', 'Gujarat', 'Chandigarh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2017)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Haryana, Gujarat, UP – 2017', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Haryana', 'Gujarat', 'Chandigarh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2017)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Haryana, Gujarat, UP – 2017', width=550, height=320)
     return chart


</code>
"
3201,temporal_aggregation,Show the monthly average PM2.5 for Kohima in 2023 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Kohima') & (data['Timestamp'].dt.year == 2023)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Kohima 2023', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Kohima') & (data['Timestamp'].dt.year == 2023)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Kohima 2023', width=450, height=280)


</code>
"
3202,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Nagaland, Jharkhand, and Jharkhand in 2021 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Nagaland', 'Jharkhand', 'Jharkhand']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Nagaland, Jharkhand, UP – 2021', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Nagaland', 'Jharkhand', 'Jharkhand']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Nagaland, Jharkhand, UP – 2021', width=550, height=320)
     return chart


</code>
"
3203,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Uttarakhand, Assam, and Tamil Nadu from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Uttarakhand', 'Assam', 'Tamil Nadu']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Uttarakhand vs Assam vs Tamil Nadu', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Uttarakhand', 'Assam', 'Tamil Nadu']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Uttarakhand vs Assam vs Tamil Nadu', width=550, height=320)
     return chart


</code>
"
3204,temporal_aggregation,Show a monthly bar chart of the number of days Mizoram exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2023.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Mizoram') & (data['Timestamp'].dt.year == 2023)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Mizoram Exceeded WHO PM2.5 Guideline per Month – 2023', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Mizoram') & (data['Timestamp'].dt.year == 2023)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Mizoram Exceeded WHO PM2.5 Guideline per Month – 2023', width=500, height=300)
     return chart


</code>
"
3205,temporal_aggregation,Show the monthly average PM10 trend for Mandi Gobindgarh from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Mandi Gobindgarh'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Mandi Gobindgarh (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Mandi Gobindgarh'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Mandi Gobindgarh (2019–2024)', width=600, height=300)
     return chart


</code>
"
3206,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Puducherry, Assam, and Nagaland across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Puducherry', 'Assam', 'Nagaland']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Puducherry', 'Assam', 'Nagaland']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
3207,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Telangana, Chandigarh, and Nagaland from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Telangana', 'Chandigarh', 'Nagaland']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Telangana vs Chandigarh vs Nagaland', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Telangana', 'Chandigarh', 'Nagaland']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Telangana vs Chandigarh vs Nagaland', width=550, height=320)
     return chart


</code>
"
3208,temporal_aggregation,Show the monthly average PM2.5 for Jhunjhunu in 2017 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Jhunjhunu') & (data['Timestamp'].dt.year == 2017)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Jhunjhunu 2017', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Jhunjhunu') & (data['Timestamp'].dt.year == 2017)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Jhunjhunu 2017', width=450, height=280)


</code>
"
3209,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Meghalaya, Kerala, and Manipur in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Meghalaya', 'Kerala', 'Manipur']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Meghalaya, Kerala, UP – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Meghalaya', 'Kerala', 'Manipur']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Meghalaya, Kerala, UP – 2024', width=550, height=320)
     return chart


</code>
"
3210,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Arunachal Pradesh, Karnataka, and Bihar in 2019 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Arunachal Pradesh', 'Karnataka', 'Bihar']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Arunachal Pradesh, Karnataka, UP – 2019', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Arunachal Pradesh', 'Karnataka', 'Bihar']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Arunachal Pradesh, Karnataka, UP – 2019', width=550, height=320)
     return chart


</code>
"
3211,temporal_aggregation,Show a monthly bar chart of the number of days Tamil Nadu exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2020.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Tamil Nadu') & (data['Timestamp'].dt.year == 2020)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Tamil Nadu Exceeded WHO PM2.5 Guideline per Month – 2020', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Tamil Nadu') & (data['Timestamp'].dt.year == 2020)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Tamil Nadu Exceeded WHO PM2.5 Guideline per Month – 2020', width=500, height=300)
     return chart


</code>
"
3212,temporal_aggregation,Show the monthly average PM2.5 for Bettiah in 2023 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Bettiah') & (data['Timestamp'].dt.year == 2023)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Bettiah 2023', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Bettiah') & (data['Timestamp'].dt.year == 2023)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Bettiah 2023', width=450, height=280)


</code>
"
3213,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Telangana, Andhra Pradesh, and Telangana from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Telangana', 'Andhra Pradesh', 'Telangana']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Telangana vs Andhra Pradesh vs Telangana', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Telangana', 'Andhra Pradesh', 'Telangana']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Telangana vs Andhra Pradesh vs Telangana', width=550, height=320)
     return chart


</code>
"
3214,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Puducherry, Mizoram, and Assam in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Puducherry', 'Mizoram', 'Assam']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Puducherry, Mizoram, UP – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Puducherry', 'Mizoram', 'Assam']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Puducherry, Mizoram, UP – 2022', width=550, height=320)
     return chart


</code>
"
3215,specific_pattern,Show a cumulative area chart of PM2.5 readings for Bikaner across 2023.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Bikaner') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Bikaner 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Bikaner') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Bikaner 2023', width=600, height=300)
     return chart


</code>
"
3216,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Haryana, Jharkhand, and Meghalaya across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Haryana', 'Jharkhand', 'Meghalaya']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Haryana', 'Jharkhand', 'Meghalaya']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
3217,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Jodhpur, Munger, and Kolhapur in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Jodhpur', 'Munger', 'Kolhapur']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Jodhpur vs Munger vs Kolhapur – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Jodhpur', 'Munger', 'Kolhapur']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Jodhpur vs Munger vs Kolhapur – 2024', width=550, height=320)
     return chart


</code>
"
3218,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Rajasthan, Madhya Pradesh, and Puducherry in 2021 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Rajasthan', 'Madhya Pradesh', 'Puducherry']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Rajasthan, Madhya Pradesh, UP – 2021', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Rajasthan', 'Madhya Pradesh', 'Puducherry']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Rajasthan, Madhya Pradesh, UP – 2021', width=550, height=320)
     return chart


</code>
"
3219,specific_pattern,Plot the rolling 30-day average PM2.5 for Manipur in 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Manipur') & (data['Timestamp'].dt.year == 2022)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Manipur 2022', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Manipur') & (data['Timestamp'].dt.year == 2022)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Manipur 2022', width=600, height=300)


</code>
"
3220,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Mizoram, Karnataka, and Jharkhand across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Mizoram', 'Karnataka', 'Jharkhand']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Mizoram', 'Karnataka', 'Jharkhand']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
3221,temporal_aggregation,Plot the weekly average PM2.5 for Katni in 2023 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Katni') & (data['Timestamp'].dt.year == 2023)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Katni 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Katni') & (data['Timestamp'].dt.year == 2023)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Katni 2023', width=600, height=300)
     return chart


</code>
"
3222,temporal_aggregation,Plot the weekly average PM2.5 for Hapur in 2023 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Hapur') & (data['Timestamp'].dt.year == 2023)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Hapur 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Hapur') & (data['Timestamp'].dt.year == 2023)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Hapur 2023', width=600, height=300)
     return chart


</code>
"
3223,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Uttar Pradesh stations in 2018, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Uttar Pradesh') & (data['Timestamp'].dt.year == 2018)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Uttar Pradesh Stations 2018', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Uttar Pradesh') & (data['Timestamp'].dt.year == 2018)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Uttar Pradesh Stations 2018', width=450, height=350)


</code>
"
3224,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Maharashtra stations in 2019, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Maharashtra') & (data['Timestamp'].dt.year == 2019)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Maharashtra Stations 2019', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Maharashtra') & (data['Timestamp'].dt.year == 2019)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Maharashtra Stations 2019', width=450, height=350)


</code>
"
3225,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Andhra Pradesh, Meghalaya, and Haryana from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Andhra Pradesh', 'Meghalaya', 'Haryana']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Andhra Pradesh vs Meghalaya vs Haryana', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Andhra Pradesh', 'Meghalaya', 'Haryana']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Andhra Pradesh vs Meghalaya vs Haryana', width=550, height=320)
     return chart


</code>
"
3226,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Nagaland, Nagaland, and Puducherry from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Nagaland', 'Nagaland', 'Puducherry']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Nagaland vs Nagaland vs Puducherry', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Nagaland', 'Nagaland', 'Puducherry']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Nagaland vs Nagaland vs Puducherry', width=550, height=320)
     return chart


</code>
"
3227,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Karnataka, Gujarat, and Rajasthan from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Karnataka', 'Gujarat', 'Rajasthan']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Karnataka vs Gujarat vs Rajasthan', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Karnataka', 'Gujarat', 'Rajasthan']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Karnataka vs Gujarat vs Rajasthan', width=550, height=320)
     return chart


</code>
"
3228,specific_pattern,Show a cumulative area chart of PM2.5 readings for Bhagalpur across 2024.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Bhagalpur') & (data['Timestamp'].dt.year == 2024)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Bhagalpur 2024', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Bhagalpur') & (data['Timestamp'].dt.year == 2024)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Bhagalpur 2024', width=600, height=300)
     return chart


</code>
"
3229,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Haryana, Arunachal Pradesh, and Assam from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Haryana', 'Arunachal Pradesh', 'Assam']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Haryana vs Arunachal Pradesh vs Assam', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Haryana', 'Arunachal Pradesh', 'Assam']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Haryana vs Arunachal Pradesh vs Assam', width=550, height=320)
     return chart


</code>
"
3230,specific_pattern,Show a cumulative area chart of PM2.5 readings for Palwal across 2022.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Palwal') & (data['Timestamp'].dt.year == 2022)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Palwal 2022', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Palwal') & (data['Timestamp'].dt.year == 2022)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Palwal 2022', width=600, height=300)
     return chart


</code>
"
3231,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Ramanathapuram, Kashipur, and Shillong in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Ramanathapuram', 'Kashipur', 'Shillong']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Ramanathapuram vs Kashipur vs Shillong – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Ramanathapuram', 'Kashipur', 'Shillong']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Ramanathapuram vs Kashipur vs Shillong – 2024', width=550, height=320)
     return chart


</code>
"
3232,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Manipur stations in 2019, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Manipur') & (data['Timestamp'].dt.year == 2019)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Manipur Stations 2019', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Manipur') & (data['Timestamp'].dt.year == 2019)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Manipur Stations 2019', width=450, height=350)


</code>
"
3233,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Delhi, Kerala, and Telangana in 2017 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Delhi', 'Kerala', 'Telangana']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2017)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Delhi, Kerala, UP – 2017', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Delhi', 'Kerala', 'Telangana']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2017)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Delhi, Kerala, UP – 2017', width=550, height=320)
     return chart


</code>
"
3234,temporal_aggregation,Show a monthly bar chart of the number of days Arunachal Pradesh exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2023.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Arunachal Pradesh') & (data['Timestamp'].dt.year == 2023)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Arunachal Pradesh Exceeded WHO PM2.5 Guideline per Month – 2023', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Arunachal Pradesh') & (data['Timestamp'].dt.year == 2023)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Arunachal Pradesh Exceeded WHO PM2.5 Guideline per Month – 2023', width=500, height=300)
     return chart


</code>
"
3235,temporal_aggregation,Plot the weekly average PM2.5 for Akola in 2023 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Akola') & (data['Timestamp'].dt.year == 2023)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Akola 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Akola') & (data['Timestamp'].dt.year == 2023)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Akola 2023', width=600, height=300)
     return chart


</code>
"
3236,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Jharkhand, Tamil Nadu, and Delhi across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Jharkhand', 'Tamil Nadu', 'Delhi']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Jharkhand', 'Tamil Nadu', 'Delhi']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
3237,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Tamil Nadu, Delhi, and Punjab from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Tamil Nadu', 'Delhi', 'Punjab']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Tamil Nadu vs Delhi vs Punjab', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Tamil Nadu', 'Delhi', 'Punjab']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Tamil Nadu vs Delhi vs Punjab', width=550, height=320)
     return chart


</code>
"
3238,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Nagaland, Maharashtra, and Tripura in 2017 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Nagaland', 'Maharashtra', 'Tripura']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2017)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Nagaland, Maharashtra, UP – 2017', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Nagaland', 'Maharashtra', 'Tripura']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2017)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Nagaland, Maharashtra, UP – 2017', width=550, height=320)
     return chart


</code>
"
3239,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Jammu and Kashmir, Kerala, and Punjab from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Jammu and Kashmir', 'Kerala', 'Punjab']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Jammu and Kashmir vs Kerala vs Punjab', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Jammu and Kashmir', 'Kerala', 'Punjab']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Jammu and Kashmir vs Kerala vs Punjab', width=550, height=320)
     return chart


</code>
"
3240,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Uttarakhand, Nagaland, and Mizoram in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Uttarakhand', 'Nagaland', 'Mizoram']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Uttarakhand, Nagaland, UP – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Uttarakhand', 'Nagaland', 'Mizoram']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Uttarakhand, Nagaland, UP – 2023', width=550, height=320)
     return chart


</code>
"
3241,temporal_aggregation,Plot the weekly average PM2.5 for Darbhanga in 2021 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Darbhanga') & (data['Timestamp'].dt.year == 2021)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Darbhanga 2021', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Darbhanga') & (data['Timestamp'].dt.year == 2021)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Darbhanga 2021', width=600, height=300)
     return chart


</code>
"
3242,specific_pattern,Show a cumulative area chart of PM2.5 readings for Faridabad across 2023.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Faridabad') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Faridabad 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Faridabad') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Faridabad 2023', width=600, height=300)
     return chart


</code>
"
3243,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Guwahati, Bihar Sharif, and Jodhpur in 2019 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Guwahati', 'Bihar Sharif', 'Jodhpur']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Guwahati vs Bihar Sharif vs Jodhpur – 2019', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Guwahati', 'Bihar Sharif', 'Jodhpur']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Guwahati vs Bihar Sharif vs Jodhpur – 2019', width=550, height=320)
     return chart


</code>
"
3244,specific_pattern,Show a cumulative area chart of PM2.5 readings for Kolkata across 2023.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Kolkata') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Kolkata 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Kolkata') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Kolkata 2023', width=600, height=300)
     return chart


</code>
"
3245,temporal_aggregation,Show the monthly average PM2.5 for Ratlam in 2020 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Ratlam') & (data['Timestamp'].dt.year == 2020)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Ratlam 2020', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Ratlam') & (data['Timestamp'].dt.year == 2020)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Ratlam 2020', width=450, height=280)


</code>
"
3246,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Tamil Nadu, Kerala, and Arunachal Pradesh from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Tamil Nadu', 'Kerala', 'Arunachal Pradesh']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Tamil Nadu vs Kerala vs Arunachal Pradesh', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Tamil Nadu', 'Kerala', 'Arunachal Pradesh']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Tamil Nadu vs Kerala vs Arunachal Pradesh', width=550, height=320)
     return chart


</code>
"
3247,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Himachal Pradesh, Jammu and Kashmir, and Uttar Pradesh in 2021 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Himachal Pradesh', 'Jammu and Kashmir', 'Uttar Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Himachal Pradesh, Jammu and Kashmir, UP – 2021', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Himachal Pradesh', 'Jammu and Kashmir', 'Uttar Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Himachal Pradesh, Jammu and Kashmir, UP – 2021', width=550, height=320)
     return chart


</code>
"
3248,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Sikkim, Telangana, and Jharkhand in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Sikkim', 'Telangana', 'Jharkhand']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Sikkim, Telangana, UP – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Sikkim', 'Telangana', 'Jharkhand']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Sikkim, Telangana, UP – 2024', width=550, height=320)
     return chart


</code>
"
3249,temporal_aggregation,Show the monthly average PM2.5 for Ujjain in 2022 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Ujjain') & (data['Timestamp'].dt.year == 2022)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Ujjain 2022', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Ujjain') & (data['Timestamp'].dt.year == 2022)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Ujjain 2022', width=450, height=280)


</code>
"
3250,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Punjab, Karnataka, and Kerala in 2021 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Punjab', 'Karnataka', 'Kerala']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Punjab, Karnataka, UP – 2021', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Punjab', 'Karnataka', 'Kerala']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Punjab, Karnataka, UP – 2021', width=550, height=320)
     return chart


</code>
"
3251,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Sikkim, Mizoram, and Chhattisgarh across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Sikkim', 'Mizoram', 'Chhattisgarh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Sikkim', 'Mizoram', 'Chhattisgarh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
3252,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Gujarat, Karnataka, and Meghalaya from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Gujarat', 'Karnataka', 'Meghalaya']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Gujarat vs Karnataka vs Meghalaya', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Gujarat', 'Karnataka', 'Meghalaya']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Gujarat vs Karnataka vs Meghalaya', width=550, height=320)
     return chart


</code>
"
3253,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Tamil Nadu, Chandigarh, and Himachal Pradesh from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Tamil Nadu', 'Chandigarh', 'Himachal Pradesh']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Tamil Nadu vs Chandigarh vs Himachal Pradesh', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Tamil Nadu', 'Chandigarh', 'Himachal Pradesh']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Tamil Nadu vs Chandigarh vs Himachal Pradesh', width=550, height=320)
     return chart


</code>
"
3254,specific_pattern,Plot the rolling 30-day average PM2.5 for Manipur in 2018 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Manipur') & (data['Timestamp'].dt.year == 2018)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Manipur 2018', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Manipur') & (data['Timestamp'].dt.year == 2018)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Manipur 2018', width=600, height=300)


</code>
"
3255,specific_pattern,Plot the rolling 30-day average PM2.5 for Meghalaya in 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Meghalaya') & (data['Timestamp'].dt.year == 2022)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Meghalaya 2022', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Meghalaya') & (data['Timestamp'].dt.year == 2022)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Meghalaya 2022', width=600, height=300)


</code>
"
3256,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Maharashtra, Delhi, and Jharkhand in 2019 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Maharashtra', 'Delhi', 'Jharkhand']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Maharashtra, Delhi, UP – 2019', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Maharashtra', 'Delhi', 'Jharkhand']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Maharashtra, Delhi, UP – 2019', width=550, height=320)
     return chart


</code>
"
3257,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Rajasthan, Manipur, and Arunachal Pradesh across 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Rajasthan', 'Manipur', 'Arunachal Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Rajasthan', 'Manipur', 'Arunachal Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
     return chart


</code>
"
3258,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Tamil Nadu, Chhattisgarh, and Himachal Pradesh across 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Tamil Nadu', 'Chhattisgarh', 'Himachal Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Tamil Nadu', 'Chhattisgarh', 'Himachal Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
     return chart


</code>
"
3259,temporal_aggregation,Plot the weekly average PM2.5 for Amritsar in 2019 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Amritsar') & (data['Timestamp'].dt.year == 2019)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Amritsar 2019', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Amritsar') & (data['Timestamp'].dt.year == 2019)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Amritsar 2019', width=600, height=300)
     return chart


</code>
"
3260,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Tamil Nadu, Kerala, and Uttar Pradesh in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Tamil Nadu', 'Kerala', 'Uttar Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Tamil Nadu, Kerala, UP – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Tamil Nadu', 'Kerala', 'Uttar Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Tamil Nadu, Kerala, UP – 2022', width=550, height=320)
     return chart


</code>
"
3261,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Jammu and Kashmir, Nagaland, and Delhi across 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Jammu and Kashmir', 'Nagaland', 'Delhi']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Jammu and Kashmir', 'Nagaland', 'Delhi']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
     return chart


</code>
"
3262,temporal_aggregation,Show the monthly average PM2.5 for Aurangabad in 2017 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Aurangabad') & (data['Timestamp'].dt.year == 2017)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Aurangabad 2017', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Aurangabad') & (data['Timestamp'].dt.year == 2017)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Aurangabad 2017', width=450, height=280)


</code>
"
3263,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Madhya Pradesh, Meghalaya, and Mizoram across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Madhya Pradesh', 'Meghalaya', 'Mizoram']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Madhya Pradesh', 'Meghalaya', 'Mizoram']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
3264,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Boisar, Parbhani, and Amaravati in 2020 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Boisar', 'Parbhani', 'Amaravati']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2020)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Boisar vs Parbhani vs Amaravati – 2020', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Boisar', 'Parbhani', 'Amaravati']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2020)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Boisar vs Parbhani vs Amaravati – 2020', width=550, height=320)
     return chart


</code>
"
3265,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Bharatpur, Bhilai, and Rajamahendravaram in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Bharatpur', 'Bhilai', 'Rajamahendravaram']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Bharatpur vs Bhilai vs Rajamahendravaram – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Bharatpur', 'Bhilai', 'Rajamahendravaram']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Bharatpur vs Bhilai vs Rajamahendravaram – 2022', width=550, height=320)
     return chart


</code>
"
3266,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Puducherry, Karnataka, and Bihar across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Puducherry', 'Karnataka', 'Bihar']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Puducherry', 'Karnataka', 'Bihar']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
3267,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Himachal Pradesh, Himachal Pradesh, and Jammu and Kashmir from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Himachal Pradesh', 'Himachal Pradesh', 'Jammu and Kashmir']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Himachal Pradesh vs Himachal Pradesh vs Jammu and Kashmir', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Himachal Pradesh', 'Himachal Pradesh', 'Jammu and Kashmir']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Himachal Pradesh vs Himachal Pradesh vs Jammu and Kashmir', width=550, height=320)
     return chart


</code>
"
3268,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Sikkim, Tripura, and Karnataka across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Sikkim', 'Tripura', 'Karnataka']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Sikkim', 'Tripura', 'Karnataka']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
3269,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Himachal Pradesh, Odisha, and Mizoram in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Himachal Pradesh', 'Odisha', 'Mizoram']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Himachal Pradesh, Odisha, UP – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Himachal Pradesh', 'Odisha', 'Mizoram']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Himachal Pradesh, Odisha, UP – 2022', width=550, height=320)
     return chart


</code>
"
3270,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Kerala, Rajasthan, and Tripura in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Kerala', 'Rajasthan', 'Tripura']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Kerala, Rajasthan, UP – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Kerala', 'Rajasthan', 'Tripura']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Kerala, Rajasthan, UP – 2024', width=550, height=320)
     return chart


</code>
"
3271,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Andhra Pradesh, Telangana, and Rajasthan from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Andhra Pradesh', 'Telangana', 'Rajasthan']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Andhra Pradesh vs Telangana vs Rajasthan', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Andhra Pradesh', 'Telangana', 'Rajasthan']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Andhra Pradesh vs Telangana vs Rajasthan', width=550, height=320)
     return chart


</code>
"
3272,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Arunachal Pradesh stations in 2018, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Arunachal Pradesh') & (data['Timestamp'].dt.year == 2018)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Arunachal Pradesh Stations 2018', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Arunachal Pradesh') & (data['Timestamp'].dt.year == 2018)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Arunachal Pradesh Stations 2018', width=450, height=350)


</code>
"
3273,temporal_aggregation,Show the monthly average PM2.5 for Pratapgarh in 2023 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Pratapgarh') & (data['Timestamp'].dt.year == 2023)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Pratapgarh 2023', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Pratapgarh') & (data['Timestamp'].dt.year == 2023)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Pratapgarh 2023', width=450, height=280)


</code>
"
3274,specific_pattern,Plot the rolling 30-day average PM2.5 for Delhi in 2019 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Delhi') & (data['Timestamp'].dt.year == 2019)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Delhi 2019', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Delhi') & (data['Timestamp'].dt.year == 2019)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Delhi 2019', width=600, height=300)


</code>
"
3275,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Jalgaon, Naharlagun, and Talcher in 2020 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Jalgaon', 'Naharlagun', 'Talcher']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2020)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Jalgaon vs Naharlagun vs Talcher – 2020', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Jalgaon', 'Naharlagun', 'Talcher']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2020)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Jalgaon vs Naharlagun vs Talcher – 2020', width=550, height=320)
     return chart


</code>
"
3276,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Bihar, Assam, and Meghalaya from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Bihar', 'Assam', 'Meghalaya']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Bihar vs Assam vs Meghalaya', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Bihar', 'Assam', 'Meghalaya']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Bihar vs Assam vs Meghalaya', width=550, height=320)
     return chart


</code>
"
3277,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Jharkhand, Puducherry, and Madhya Pradesh from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Jharkhand', 'Puducherry', 'Madhya Pradesh']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Jharkhand vs Puducherry vs Madhya Pradesh', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Jharkhand', 'Puducherry', 'Madhya Pradesh']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Jharkhand vs Puducherry vs Madhya Pradesh', width=550, height=320)
     return chart


</code>
"
3278,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Telangana, Uttar Pradesh, and Bihar from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Telangana', 'Uttar Pradesh', 'Bihar']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Telangana vs Uttar Pradesh vs Bihar', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Telangana', 'Uttar Pradesh', 'Bihar']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Telangana vs Uttar Pradesh vs Bihar', width=550, height=320)
     return chart


</code>
"
3279,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Puducherry, Uttar Pradesh, and Kerala across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Puducherry', 'Uttar Pradesh', 'Kerala']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Puducherry', 'Uttar Pradesh', 'Kerala']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
3280,temporal_aggregation,Show the monthly average PM10 trend for Jalgaon from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Jalgaon'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Jalgaon (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Jalgaon'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Jalgaon (2019–2024)', width=600, height=300)
     return chart


</code>
"
3281,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Manipur, Telangana, and Punjab in 2019 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Manipur', 'Telangana', 'Punjab']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Manipur, Telangana, UP – 2019', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Manipur', 'Telangana', 'Punjab']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Manipur, Telangana, UP – 2019', width=550, height=320)
     return chart


</code>
"
3282,temporal_aggregation,Show the monthly average PM2.5 for Rupnagar in 2020 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Rupnagar') & (data['Timestamp'].dt.year == 2020)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Rupnagar 2020', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Rupnagar') & (data['Timestamp'].dt.year == 2020)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Rupnagar 2020', width=450, height=280)


</code>
"
3283,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Sawai Madhopur, Jaipur, and Ankleshwar in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Sawai Madhopur', 'Jaipur', 'Ankleshwar']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Sawai Madhopur vs Jaipur vs Ankleshwar – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Sawai Madhopur', 'Jaipur', 'Ankleshwar']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Sawai Madhopur vs Jaipur vs Ankleshwar – 2024', width=550, height=320)
     return chart


</code>
"
3284,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Shivamogga, Amritsar, and Cuttack in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Shivamogga', 'Amritsar', 'Cuttack']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Shivamogga vs Amritsar vs Cuttack – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Shivamogga', 'Amritsar', 'Cuttack']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Shivamogga vs Amritsar vs Cuttack – 2024', width=550, height=320)
     return chart


</code>
"
3285,temporal_aggregation,Show the monthly average PM2.5 for Bhiwandi in 2019 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Bhiwandi') & (data['Timestamp'].dt.year == 2019)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Bhiwandi 2019', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Bhiwandi') & (data['Timestamp'].dt.year == 2019)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Bhiwandi 2019', width=450, height=280)


</code>
"
3286,temporal_aggregation,Plot the weekly average PM2.5 for Guwahati in 2023 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Guwahati') & (data['Timestamp'].dt.year == 2023)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Guwahati 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Guwahati') & (data['Timestamp'].dt.year == 2023)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Guwahati 2023', width=600, height=300)
     return chart


</code>
"
3287,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Rajasthan, Himachal Pradesh, and Haryana in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Rajasthan', 'Himachal Pradesh', 'Haryana']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Rajasthan, Himachal Pradesh, UP – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Rajasthan', 'Himachal Pradesh', 'Haryana']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Rajasthan, Himachal Pradesh, UP – 2023', width=550, height=320)
     return chart


</code>
"
3288,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Begusarai, Bhagalpur, and Ankleshwar in 2020 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Begusarai', 'Bhagalpur', 'Ankleshwar']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2020)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Begusarai vs Bhagalpur vs Ankleshwar – 2020', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Begusarai', 'Bhagalpur', 'Ankleshwar']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2020)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Begusarai vs Bhagalpur vs Ankleshwar – 2020', width=550, height=320)
     return chart


</code>
"
3289,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Mizoram, Himachal Pradesh, and Sikkim in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Mizoram', 'Himachal Pradesh', 'Sikkim']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Mizoram, Himachal Pradesh, UP – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Mizoram', 'Himachal Pradesh', 'Sikkim']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Mizoram, Himachal Pradesh, UP – 2024', width=550, height=320)
     return chart


</code>
"
3290,temporal_aggregation,Plot the weekly average PM2.5 for Ramanathapuram in 2023 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Ramanathapuram') & (data['Timestamp'].dt.year == 2023)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Ramanathapuram 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Ramanathapuram') & (data['Timestamp'].dt.year == 2023)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Ramanathapuram 2023', width=600, height=300)
     return chart


</code>
"
3291,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Himachal Pradesh, Maharashtra, and Rajasthan across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Himachal Pradesh', 'Maharashtra', 'Rajasthan']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Himachal Pradesh', 'Maharashtra', 'Rajasthan']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
3292,specific_pattern,Show a cumulative area chart of PM2.5 readings for Hyderabad across 2018.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Hyderabad') & (data['Timestamp'].dt.year == 2018)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Hyderabad 2018', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Hyderabad') & (data['Timestamp'].dt.year == 2018)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Hyderabad 2018', width=600, height=300)
     return chart


</code>
"
3293,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Uttarakhand, Assam, and Chandigarh across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Uttarakhand', 'Assam', 'Chandigarh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Uttarakhand', 'Assam', 'Chandigarh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
3294,temporal_aggregation,Plot the weekly average PM2.5 for Bhiwani in 2021 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Bhiwani') & (data['Timestamp'].dt.year == 2021)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Bhiwani 2021', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Bhiwani') & (data['Timestamp'].dt.year == 2021)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Bhiwani 2021', width=600, height=300)
     return chart


</code>
"
3295,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Dharuhera, Mandi Gobindgarh, and Sri Ganganagar in 2020 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Dharuhera', 'Mandi Gobindgarh', 'Sri Ganganagar']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2020)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Dharuhera vs Mandi Gobindgarh vs Sri Ganganagar – 2020', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Dharuhera', 'Mandi Gobindgarh', 'Sri Ganganagar']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2020)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Dharuhera vs Mandi Gobindgarh vs Sri Ganganagar – 2020', width=550, height=320)
     return chart


</code>
"
3296,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Chengalpattu, Kolar, and Kohima in 2020 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Chengalpattu', 'Kolar', 'Kohima']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2020)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Chengalpattu vs Kolar vs Kohima – 2020', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Chengalpattu', 'Kolar', 'Kohima']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2020)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Chengalpattu vs Kolar vs Kohima – 2020', width=550, height=320)
     return chart


</code>
"
3297,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Telangana, Assam, and Jharkhand from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Telangana', 'Assam', 'Jharkhand']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Telangana vs Assam vs Jharkhand', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Telangana', 'Assam', 'Jharkhand']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Telangana vs Assam vs Jharkhand', width=550, height=320)
     return chart


</code>
"
3298,specific_pattern,Show a cumulative area chart of PM2.5 readings for Panipat across 2024.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Panipat') & (data['Timestamp'].dt.year == 2024)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Panipat 2024', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Panipat') & (data['Timestamp'].dt.year == 2024)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Panipat 2024', width=600, height=300)
     return chart


</code>
"
3299,temporal_aggregation,Show the monthly average PM2.5 for Pimpri-Chinchwad in 2019 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Pimpri-Chinchwad') & (data['Timestamp'].dt.year == 2019)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Pimpri-Chinchwad 2019', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Pimpri-Chinchwad') & (data['Timestamp'].dt.year == 2019)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Pimpri-Chinchwad 2019', width=450, height=280)


</code>
"
3300,temporal_aggregation,Plot the weekly average PM2.5 for Tirupati in 2021 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Tirupati') & (data['Timestamp'].dt.year == 2021)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Tirupati 2021', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Tirupati') & (data['Timestamp'].dt.year == 2021)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Tirupati 2021', width=600, height=300)
     return chart


</code>
"
3301,temporal_aggregation,Plot the weekly average PM2.5 for Gurugram in 2021 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Gurugram') & (data['Timestamp'].dt.year == 2021)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Gurugram 2021', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Gurugram') & (data['Timestamp'].dt.year == 2021)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Gurugram 2021', width=600, height=300)
     return chart


</code>
"
3302,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Himachal Pradesh, Sikkim, and Meghalaya from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Himachal Pradesh', 'Sikkim', 'Meghalaya']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Himachal Pradesh vs Sikkim vs Meghalaya', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Himachal Pradesh', 'Sikkim', 'Meghalaya']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Himachal Pradesh vs Sikkim vs Meghalaya', width=550, height=320)
     return chart


</code>
"
3303,temporal_aggregation,Show the monthly average PM2.5 for Barrackpore in 2022 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Barrackpore') & (data['Timestamp'].dt.year == 2022)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Barrackpore 2022', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Barrackpore') & (data['Timestamp'].dt.year == 2022)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Barrackpore 2022', width=450, height=280)


</code>
"
3304,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Jodhpur, Firozabad, and Narnaul in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Jodhpur', 'Firozabad', 'Narnaul']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Jodhpur vs Firozabad vs Narnaul – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Jodhpur', 'Firozabad', 'Narnaul']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Jodhpur vs Firozabad vs Narnaul – 2024', width=550, height=320)
     return chart


</code>
"
3305,temporal_aggregation,Show the monthly average PM10 trend for Korba from 2017 to 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Korba'].copy()
    df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Korba (2017–2022)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Korba'].copy()
     df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Korba (2017–2022)', width=600, height=300)
     return chart


</code>
"
3306,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Arunachal Pradesh, Tripura, and Telangana in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Arunachal Pradesh', 'Tripura', 'Telangana']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Arunachal Pradesh, Tripura, UP – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Arunachal Pradesh', 'Tripura', 'Telangana']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Arunachal Pradesh, Tripura, UP – 2024', width=550, height=320)
     return chart


</code>
"
3307,temporal_aggregation,Show the monthly average PM2.5 for Bhilai in 2018 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Bhilai') & (data['Timestamp'].dt.year == 2018)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Bhilai 2018', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Bhilai') & (data['Timestamp'].dt.year == 2018)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Bhilai 2018', width=450, height=280)


</code>
"
3308,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Nagaland stations in 2019, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Nagaland') & (data['Timestamp'].dt.year == 2019)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Nagaland Stations 2019', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Nagaland') & (data['Timestamp'].dt.year == 2019)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Nagaland Stations 2019', width=450, height=350)


</code>
"
3309,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Rajasthan, Himachal Pradesh, and Tamil Nadu across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Rajasthan', 'Himachal Pradesh', 'Tamil Nadu']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Rajasthan', 'Himachal Pradesh', 'Tamil Nadu']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
3310,specific_pattern,Show a cumulative area chart of PM2.5 readings for Nanded across 2023.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Nanded') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Nanded 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Nanded') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Nanded 2023', width=600, height=300)
     return chart


</code>
"
3311,specific_pattern,Plot the rolling 30-day average PM2.5 for Odisha in 2017 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Odisha') & (data['Timestamp'].dt.year == 2017)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Odisha 2017', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Odisha') & (data['Timestamp'].dt.year == 2017)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Odisha 2017', width=600, height=300)


</code>
"
3312,spatial_aggregation,Plot the top 10 states by average PM2.5 in 2017 as a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2017]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nlargest(10, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 10 States by Average PM2.5 in 2017', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2017]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nlargest(10, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 10 States by Average PM2.5 in 2017', width=500, height=300)
     return chart


</code>
"
3313,temporal_aggregation,Show the monthly average PM10 trend for Sivasagar from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Sivasagar'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Sivasagar (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Sivasagar'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Sivasagar (2019–2024)', width=600, height=300)
     return chart


</code>
"
3314,temporal_aggregation,Show the monthly average PM10 trend for Vijayapura from 2017 to 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Vijayapura'].copy()
    df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Vijayapura (2017–2022)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Vijayapura'].copy()
     df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Vijayapura (2017–2022)', width=600, height=300)
     return chart


</code>
"
3315,temporal_aggregation,Show the monthly average PM2.5 for Coimbatore in 2018 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Coimbatore') & (data['Timestamp'].dt.year == 2018)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Coimbatore 2018', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Coimbatore') & (data['Timestamp'].dt.year == 2018)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Coimbatore 2018', width=450, height=280)


</code>
"
3316,temporal_aggregation,Show the monthly average PM10 trend for Gadag from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Gadag'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Gadag (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Gadag'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Gadag (2019–2024)', width=600, height=300)
     return chart


</code>
"
3317,specific_pattern,Show a cumulative area chart of PM2.5 readings for Mandideep across 2022.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Mandideep') & (data['Timestamp'].dt.year == 2022)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Mandideep 2022', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Mandideep') & (data['Timestamp'].dt.year == 2022)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Mandideep 2022', width=600, height=300)
     return chart


</code>
"
3318,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Navi Mumbai, Bhubaneswar, and Asansol in 2018 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Navi Mumbai', 'Bhubaneswar', 'Asansol']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2018)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Navi Mumbai vs Bhubaneswar vs Asansol – 2018', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Navi Mumbai', 'Bhubaneswar', 'Asansol']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2018)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Navi Mumbai vs Bhubaneswar vs Asansol – 2018', width=550, height=320)
     return chart


</code>
"
3319,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Gujarat, Meghalaya, and Manipur across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Gujarat', 'Meghalaya', 'Manipur']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Gujarat', 'Meghalaya', 'Manipur']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
3320,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Assam, Karnataka, and Delhi across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Assam', 'Karnataka', 'Delhi']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Assam', 'Karnataka', 'Delhi']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
3321,temporal_aggregation,Show the monthly average PM2.5 for Nanded in 2022 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Nanded') & (data['Timestamp'].dt.year == 2022)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Nanded 2022', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Nanded') & (data['Timestamp'].dt.year == 2022)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Nanded 2022', width=450, height=280)


</code>
"
3322,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Jalgaon, Latur, and Thrissur in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Jalgaon', 'Latur', 'Thrissur']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Jalgaon vs Latur vs Thrissur – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Jalgaon', 'Latur', 'Thrissur']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Jalgaon vs Latur vs Thrissur – 2024', width=550, height=320)
     return chart


</code>
"
3323,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Himachal Pradesh, Chandigarh, and Madhya Pradesh from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Himachal Pradesh', 'Chandigarh', 'Madhya Pradesh']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Himachal Pradesh vs Chandigarh vs Madhya Pradesh', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Himachal Pradesh', 'Chandigarh', 'Madhya Pradesh']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Himachal Pradesh vs Chandigarh vs Madhya Pradesh', width=550, height=320)
     return chart


</code>
"
3324,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Chandigarh, Rajasthan, and Puducherry from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Chandigarh', 'Rajasthan', 'Puducherry']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Chandigarh vs Rajasthan vs Puducherry', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Chandigarh', 'Rajasthan', 'Puducherry']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Chandigarh vs Rajasthan vs Puducherry', width=550, height=320)
     return chart


</code>
"
3325,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Palwal , Thiruvananthapuram, and Tensa in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Palwal ', 'Thiruvananthapuram', 'Tensa']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Palwal  vs Thiruvananthapuram vs Tensa – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Palwal ', 'Thiruvananthapuram', 'Tensa']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Palwal  vs Thiruvananthapuram vs Tensa – 2024', width=550, height=320)
     return chart


</code>
"
3326,specific_pattern,Show a cumulative area chart of PM2.5 readings for Cuttack across 2023.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Cuttack') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Cuttack 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Cuttack') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Cuttack 2023', width=600, height=300)
     return chart


</code>
"
3327,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Arunachal Pradesh, Delhi, and Kerala in 2019 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Arunachal Pradesh', 'Delhi', 'Kerala']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Arunachal Pradesh, Delhi, UP – 2019', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Arunachal Pradesh', 'Delhi', 'Kerala']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Arunachal Pradesh, Delhi, UP – 2019', width=550, height=320)
     return chart


</code>
"
3328,specific_pattern,Plot the rolling 30-day average PM2.5 for Kerala in 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Kerala') & (data['Timestamp'].dt.year == 2024)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Kerala 2024', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Kerala') & (data['Timestamp'].dt.year == 2024)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Kerala 2024', width=600, height=300)


</code>
"
3329,specific_pattern,Plot the rolling 30-day average PM2.5 for Punjab in 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Punjab') & (data['Timestamp'].dt.year == 2024)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Punjab 2024', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Punjab') & (data['Timestamp'].dt.year == 2024)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Punjab 2024', width=600, height=300)


</code>
"
3330,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Panipat, Kalyan, and Hisar in 2019 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Panipat', 'Kalyan', 'Hisar']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Panipat vs Kalyan vs Hisar – 2019', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Panipat', 'Kalyan', 'Hisar']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Panipat vs Kalyan vs Hisar – 2019', width=550, height=320)
     return chart


</code>
"
3331,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Uttar Pradesh, Nagaland, and Bihar across 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Uttar Pradesh', 'Nagaland', 'Bihar']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Uttar Pradesh', 'Nagaland', 'Bihar']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
     return chart


</code>
"
3332,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Uttarakhand, Andhra Pradesh, and Rajasthan across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Uttarakhand', 'Andhra Pradesh', 'Rajasthan']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Uttarakhand', 'Andhra Pradesh', 'Rajasthan']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
3333,temporal_aggregation,Show the monthly average PM2.5 for Aurangabad in 2024 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Aurangabad') & (data['Timestamp'].dt.year == 2024)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Aurangabad 2024', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Aurangabad') & (data['Timestamp'].dt.year == 2024)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Aurangabad 2024', width=450, height=280)


</code>
"
3334,temporal_aggregation,Plot the weekly average PM2.5 for Dharuhera in 2019 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Dharuhera') & (data['Timestamp'].dt.year == 2019)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Dharuhera 2019', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Dharuhera') & (data['Timestamp'].dt.year == 2019)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Dharuhera 2019', width=600, height=300)
     return chart


</code>
"
3335,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Madhya Pradesh, Gujarat, and Rajasthan from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Madhya Pradesh', 'Gujarat', 'Rajasthan']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Madhya Pradesh vs Gujarat vs Rajasthan', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Madhya Pradesh', 'Gujarat', 'Rajasthan']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Madhya Pradesh vs Gujarat vs Rajasthan', width=550, height=320)
     return chart


</code>
"
3336,temporal_aggregation,Show the monthly average PM10 trend for Chikkaballapur from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Chikkaballapur'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Chikkaballapur (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Chikkaballapur'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Chikkaballapur (2019–2024)', width=600, height=300)
     return chart


</code>
"
3337,temporal_aggregation,Plot the weekly average PM2.5 for Kohima in 2020 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Kohima') & (data['Timestamp'].dt.year == 2020)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Kohima 2020', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Kohima') & (data['Timestamp'].dt.year == 2020)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Kohima 2020', width=600, height=300)
     return chart


</code>
"
3338,specific_pattern,Plot the rolling 30-day average PM2.5 for Tamil Nadu in 2018 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Tamil Nadu') & (data['Timestamp'].dt.year == 2018)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Tamil Nadu 2018', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Tamil Nadu') & (data['Timestamp'].dt.year == 2018)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Tamil Nadu 2018', width=600, height=300)


</code>
"
3339,temporal_aggregation,Show the monthly average PM10 trend for Dehradun from 2017 to 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Dehradun'].copy()
    df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Dehradun (2017–2022)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Dehradun'].copy()
     df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Dehradun (2017–2022)', width=600, height=300)
     return chart


</code>
"
3340,temporal_aggregation,Show a monthly bar chart of the number of days Delhi exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2020.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Delhi') & (data['Timestamp'].dt.year == 2020)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Delhi Exceeded WHO PM2.5 Guideline per Month – 2020', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Delhi') & (data['Timestamp'].dt.year == 2020)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Delhi Exceeded WHO PM2.5 Guideline per Month – 2020', width=500, height=300)
     return chart


</code>
"
3341,temporal_aggregation,Plot the weekly average PM2.5 for Kolkata in 2023 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Kolkata') & (data['Timestamp'].dt.year == 2023)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Kolkata 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Kolkata') & (data['Timestamp'].dt.year == 2023)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Kolkata 2023', width=600, height=300)
     return chart


</code>
"
3342,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Sikkim, Meghalaya, and Kerala from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Sikkim', 'Meghalaya', 'Kerala']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Sikkim vs Meghalaya vs Kerala', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Sikkim', 'Meghalaya', 'Kerala']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Sikkim vs Meghalaya vs Kerala', width=550, height=320)
     return chart


</code>
"
3343,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Bihar, Tripura, and Jharkhand in 2019 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Bihar', 'Tripura', 'Jharkhand']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Bihar, Tripura, UP – 2019', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Bihar', 'Tripura', 'Jharkhand']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Bihar, Tripura, UP – 2019', width=550, height=320)
     return chart


</code>
"
3344,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Katni, Hubballi, and Rishikesh in 2020 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Katni', 'Hubballi', 'Rishikesh']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2020)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Katni vs Hubballi vs Rishikesh – 2020', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Katni', 'Hubballi', 'Rishikesh']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2020)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Katni vs Hubballi vs Rishikesh – 2020', width=550, height=320)
     return chart


</code>
"
3345,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Telangana, Assam, and Uttar Pradesh in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Telangana', 'Assam', 'Uttar Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Telangana, Assam, UP – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Telangana', 'Assam', 'Uttar Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Telangana, Assam, UP – 2023', width=550, height=320)
     return chart


</code>
"
3346,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Meghalaya, Chandigarh, and Jharkhand in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Meghalaya', 'Chandigarh', 'Jharkhand']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Meghalaya, Chandigarh, UP – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Meghalaya', 'Chandigarh', 'Jharkhand']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Meghalaya, Chandigarh, UP – 2024', width=550, height=320)
     return chart


</code>
"
3347,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Pathardih, Gangtok, and Byrnihat in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Pathardih', 'Gangtok', 'Byrnihat']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Pathardih vs Gangtok vs Byrnihat – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Pathardih', 'Gangtok', 'Byrnihat']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Pathardih vs Gangtok vs Byrnihat – 2023', width=550, height=320)
     return chart


</code>
"
3348,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for West Bengal, Jammu and Kashmir, and Manipur across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['West Bengal', 'Jammu and Kashmir', 'Manipur']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['West Bengal', 'Jammu and Kashmir', 'Manipur']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
3349,specific_pattern,Plot the rolling 30-day average PM2.5 for Jharkhand in 2018 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Jharkhand') & (data['Timestamp'].dt.year == 2018)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Jharkhand 2018', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Jharkhand') & (data['Timestamp'].dt.year == 2018)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Jharkhand 2018', width=600, height=300)


</code>
"
3350,temporal_aggregation,Show the monthly average PM2.5 for Pudukottai in 2024 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Pudukottai') & (data['Timestamp'].dt.year == 2024)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Pudukottai 2024', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Pudukottai') & (data['Timestamp'].dt.year == 2024)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Pudukottai 2024', width=450, height=280)


</code>
"
3351,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Tamil Nadu, Telangana, and Jharkhand from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Tamil Nadu', 'Telangana', 'Jharkhand']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Tamil Nadu vs Telangana vs Jharkhand', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Tamil Nadu', 'Telangana', 'Jharkhand']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Tamil Nadu vs Telangana vs Jharkhand', width=550, height=320)
     return chart


</code>
"
3352,temporal_aggregation,Show a monthly bar chart of the number of days Odisha exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2021.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Odisha') & (data['Timestamp'].dt.year == 2021)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Odisha Exceeded WHO PM2.5 Guideline per Month – 2021', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Odisha') & (data['Timestamp'].dt.year == 2021)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Odisha Exceeded WHO PM2.5 Guideline per Month – 2021', width=500, height=300)
     return chart


</code>
"
3353,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Sikkim stations in 2020, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Sikkim') & (data['Timestamp'].dt.year == 2020)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Sikkim Stations 2020', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Sikkim') & (data['Timestamp'].dt.year == 2020)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Sikkim Stations 2020', width=450, height=350)


</code>
"
3354,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Tamil Nadu, Sikkim, and Jharkhand from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Tamil Nadu', 'Sikkim', 'Jharkhand']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Tamil Nadu vs Sikkim vs Jharkhand', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Tamil Nadu', 'Sikkim', 'Jharkhand']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Tamil Nadu vs Sikkim vs Jharkhand', width=550, height=320)
     return chart


</code>
"
3355,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Tamil Nadu, Maharashtra, and Mizoram in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Tamil Nadu', 'Maharashtra', 'Mizoram']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Tamil Nadu, Maharashtra, UP – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Tamil Nadu', 'Maharashtra', 'Mizoram']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Tamil Nadu, Maharashtra, UP – 2023', width=550, height=320)
     return chart


</code>
"
3356,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Surat, Chittorgarh, and Bathinda in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Surat', 'Chittorgarh', 'Bathinda']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Surat vs Chittorgarh vs Bathinda – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Surat', 'Chittorgarh', 'Bathinda']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Surat vs Chittorgarh vs Bathinda – 2023', width=550, height=320)
     return chart


</code>
"
3357,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Assam, Chandigarh, and Tamil Nadu across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Assam', 'Chandigarh', 'Tamil Nadu']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Assam', 'Chandigarh', 'Tamil Nadu']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
3358,temporal_aggregation,Show the monthly average PM2.5 for Dharuhera in 2024 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Dharuhera') & (data['Timestamp'].dt.year == 2024)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Dharuhera 2024', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Dharuhera') & (data['Timestamp'].dt.year == 2024)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Dharuhera 2024', width=450, height=280)


</code>
"
3359,temporal_aggregation,Show the monthly average PM2.5 for Ambala in 2022 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Ambala') & (data['Timestamp'].dt.year == 2022)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Ambala 2022', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Ambala') & (data['Timestamp'].dt.year == 2022)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Ambala 2022', width=450, height=280)


</code>
"
3360,temporal_aggregation,Show the monthly average PM2.5 for Latur in 2018 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Latur') & (data['Timestamp'].dt.year == 2018)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Latur 2018', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Latur') & (data['Timestamp'].dt.year == 2018)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Latur 2018', width=450, height=280)


</code>
"
3361,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Meghalaya, Sikkim, and Gujarat across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Meghalaya', 'Sikkim', 'Gujarat']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Meghalaya', 'Sikkim', 'Gujarat']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
3362,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Kochi, Samastipur, and Pudukottai in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Kochi', 'Samastipur', 'Pudukottai']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Kochi vs Samastipur vs Pudukottai – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Kochi', 'Samastipur', 'Pudukottai']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Kochi vs Samastipur vs Pudukottai – 2022', width=550, height=320)
     return chart


</code>
"
3363,temporal_aggregation,Show the monthly average PM10 trend for Buxar from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Buxar'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Buxar (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Buxar'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Buxar (2019–2024)', width=600, height=300)
     return chart


</code>
"
3364,temporal_aggregation,Show the monthly average PM10 trend for Dholpur from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Dholpur'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Dholpur (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Dholpur'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Dholpur (2019–2024)', width=600, height=300)
     return chart


</code>
"
3365,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Delhi, Jharkhand, and Sikkim from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Delhi', 'Jharkhand', 'Sikkim']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Delhi vs Jharkhand vs Sikkim', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Delhi', 'Jharkhand', 'Sikkim']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Delhi vs Jharkhand vs Sikkim', width=550, height=320)
     return chart


</code>
"
3366,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Tripura, Jammu and Kashmir, and Assam in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Tripura', 'Jammu and Kashmir', 'Assam']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Tripura, Jammu and Kashmir, UP – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Tripura', 'Jammu and Kashmir', 'Assam']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Tripura, Jammu and Kashmir, UP – 2024', width=550, height=320)
     return chart


</code>
"
3367,temporal_aggregation,Plot the weekly average PM2.5 for Manesar in 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Manesar') & (data['Timestamp'].dt.year == 2024)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Manesar 2024', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Manesar') & (data['Timestamp'].dt.year == 2024)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Manesar 2024', width=600, height=300)
     return chart


</code>
"
3368,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Jammu and Kashmir, Sikkim, and Tamil Nadu from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Jammu and Kashmir', 'Sikkim', 'Tamil Nadu']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Jammu and Kashmir vs Sikkim vs Tamil Nadu', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Jammu and Kashmir', 'Sikkim', 'Tamil Nadu']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Jammu and Kashmir vs Sikkim vs Tamil Nadu', width=550, height=320)
     return chart


</code>
"
3369,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Madikeri, Ahmedabad, and Nanded in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Madikeri', 'Ahmedabad', 'Nanded']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Madikeri vs Ahmedabad vs Nanded – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Madikeri', 'Ahmedabad', 'Nanded']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Madikeri vs Ahmedabad vs Nanded – 2023', width=550, height=320)
     return chart


</code>
"
3370,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Kurukshetra , Virar, and Hyderabad in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Kurukshetra ', 'Virar', 'Hyderabad']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Kurukshetra  vs Virar vs Hyderabad – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Kurukshetra ', 'Virar', 'Hyderabad']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Kurukshetra  vs Virar vs Hyderabad – 2022', width=550, height=320)
     return chart


</code>
"
3371,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Himachal Pradesh, West Bengal, and Jammu and Kashmir from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Himachal Pradesh', 'West Bengal', 'Jammu and Kashmir']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Himachal Pradesh vs West Bengal vs Jammu and Kashmir', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Himachal Pradesh', 'West Bengal', 'Jammu and Kashmir']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Himachal Pradesh vs West Bengal vs Jammu and Kashmir', width=550, height=320)
     return chart


</code>
"
3372,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Telangana, Odisha, and Assam from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Telangana', 'Odisha', 'Assam']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Telangana vs Odisha vs Assam', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Telangana', 'Odisha', 'Assam']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Telangana vs Odisha vs Assam', width=550, height=320)
     return chart


</code>
"
3373,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Mahad, Ratlam, and Pithampur in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Mahad', 'Ratlam', 'Pithampur']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Mahad vs Ratlam vs Pithampur – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Mahad', 'Ratlam', 'Pithampur']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Mahad vs Ratlam vs Pithampur – 2022', width=550, height=320)
     return chart


</code>
"
3374,specific_pattern,Show a cumulative area chart of PM2.5 readings for Rairangpur across 2024.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Rairangpur') & (data['Timestamp'].dt.year == 2024)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Rairangpur 2024', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Rairangpur') & (data['Timestamp'].dt.year == 2024)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Rairangpur 2024', width=600, height=300)
     return chart


</code>
"
3375,temporal_aggregation,Plot the weekly average PM2.5 for Byasanagar in 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Byasanagar') & (data['Timestamp'].dt.year == 2024)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Byasanagar 2024', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Byasanagar') & (data['Timestamp'].dt.year == 2024)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Byasanagar 2024', width=600, height=300)
     return chart


</code>
"
3376,specific_pattern,Plot the rolling 30-day average PM2.5 for Sikkim in 2017 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Sikkim') & (data['Timestamp'].dt.year == 2017)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Sikkim 2017', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Sikkim') & (data['Timestamp'].dt.year == 2017)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Sikkim 2017', width=600, height=300)


</code>
"
3377,temporal_aggregation,Plot the weekly average PM2.5 for Ajmer in 2021 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Ajmer') & (data['Timestamp'].dt.year == 2021)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Ajmer 2021', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Ajmer') & (data['Timestamp'].dt.year == 2021)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Ajmer 2021', width=600, height=300)
     return chart


</code>
"
3378,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Manguraha, Tirupur, and Nalbari in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Manguraha', 'Tirupur', 'Nalbari']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Manguraha vs Tirupur vs Nalbari – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Manguraha', 'Tirupur', 'Nalbari']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Manguraha vs Tirupur vs Nalbari – 2023', width=550, height=320)
     return chart


</code>
"
3379,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Rajasthan, Nagaland, and Mizoram across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Rajasthan', 'Nagaland', 'Mizoram']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Rajasthan', 'Nagaland', 'Mizoram']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
3380,temporal_aggregation,Plot the weekly average PM2.5 for Yamuna Nagar in 2021 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Yamuna Nagar') & (data['Timestamp'].dt.year == 2021)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Yamuna Nagar 2021', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Yamuna Nagar') & (data['Timestamp'].dt.year == 2021)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Yamuna Nagar 2021', width=600, height=300)
     return chart


</code>
"
3381,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Karnataka stations in 2018, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Karnataka') & (data['Timestamp'].dt.year == 2018)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Karnataka Stations 2018', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Karnataka') & (data['Timestamp'].dt.year == 2018)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Karnataka Stations 2018', width=450, height=350)


</code>
"
3382,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Mizoram, Mizoram, and Madhya Pradesh from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Mizoram', 'Mizoram', 'Madhya Pradesh']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Mizoram vs Mizoram vs Madhya Pradesh', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Mizoram', 'Mizoram', 'Madhya Pradesh']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Mizoram vs Mizoram vs Madhya Pradesh', width=550, height=320)
     return chart


</code>
"
3383,temporal_aggregation,Plot the weekly average PM2.5 for Hubballi in 2021 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Hubballi') & (data['Timestamp'].dt.year == 2021)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Hubballi 2021', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Hubballi') & (data['Timestamp'].dt.year == 2021)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Hubballi 2021', width=600, height=300)
     return chart


</code>
"
3384,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Palwal, Amritsar, and Nalbari in 2019 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Palwal', 'Amritsar', 'Nalbari']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Palwal vs Amritsar vs Nalbari – 2019', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Palwal', 'Amritsar', 'Nalbari']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Palwal vs Amritsar vs Nalbari – 2019', width=550, height=320)
     return chart


</code>
"
3385,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Puducherry, Andhra Pradesh, and Uttarakhand from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Puducherry', 'Andhra Pradesh', 'Uttarakhand']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Puducherry vs Andhra Pradesh vs Uttarakhand', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Puducherry', 'Andhra Pradesh', 'Uttarakhand']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Puducherry vs Andhra Pradesh vs Uttarakhand', width=550, height=320)
     return chart


</code>
"
3386,temporal_aggregation,Show the monthly average PM10 trend for Mandideep from 2017 to 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Mandideep'].copy()
    df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Mandideep (2017–2022)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Mandideep'].copy()
     df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Mandideep (2017–2022)', width=600, height=300)
     return chart


</code>
"
3387,temporal_aggregation,Show the monthly average PM10 trend for Bhiwani from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Bhiwani'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Bhiwani (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Bhiwani'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Bhiwani (2019–2024)', width=600, height=300)
     return chart


</code>
"
3388,spatio_temporal_aggregation,"Visualize the monthly average PM10 for West Bengal, Bihar, and Mizoram in 2017 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['West Bengal', 'Bihar', 'Mizoram']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2017)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: West Bengal, Bihar, UP – 2017', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['West Bengal', 'Bihar', 'Mizoram']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2017)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: West Bengal, Bihar, UP – 2017', width=550, height=320)
     return chart


</code>
"
3389,spatial_aggregation,"Show the top 13 states by average PM10 in 2021 as a bar chart, sorted in descending order.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2021]
    df = df.groupby('state')['PM10'].mean().reset_index().dropna()
    df = df.nlargest(13, 'PM10')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Top 13 States by Average PM10 in 2021', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2021]
     df = df.groupby('state')['PM10'].mean().reset_index().dropna()
     df = df.nlargest(13, 'PM10')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM10:Q', title='Average PM10 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM10:Q', scale=alt.Scale(scheme='oranges'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Top 13 States by Average PM10 in 2021', width=500, height=300)
     return chart


</code>
"
3390,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Meghalaya, Andhra Pradesh, and Karnataka across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Meghalaya', 'Andhra Pradesh', 'Karnataka']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Meghalaya', 'Andhra Pradesh', 'Karnataka']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
3391,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Rajasthan, West Bengal, and Mizoram from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Rajasthan', 'West Bengal', 'Mizoram']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Rajasthan vs West Bengal vs Mizoram', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Rajasthan', 'West Bengal', 'Mizoram']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Rajasthan vs West Bengal vs Mizoram', width=550, height=320)
     return chart


</code>
"
3392,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Maharashtra, Uttarakhand, and Arunachal Pradesh in 2017 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Maharashtra', 'Uttarakhand', 'Arunachal Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2017)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Maharashtra, Uttarakhand, UP – 2017', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Maharashtra', 'Uttarakhand', 'Arunachal Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2017)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Maharashtra, Uttarakhand, UP – 2017', width=550, height=320)
     return chart


</code>
"
3393,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Munger, Koppal, and Davanagere in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Munger', 'Koppal', 'Davanagere']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Munger vs Koppal vs Davanagere – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Munger', 'Koppal', 'Davanagere']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Munger vs Koppal vs Davanagere – 2024', width=550, height=320)
     return chart


</code>
"
3394,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Hubballi, Ballabgarh, and Dholpur in 2019 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Hubballi', 'Ballabgarh', 'Dholpur']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Hubballi vs Ballabgarh vs Dholpur – 2019', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Hubballi', 'Ballabgarh', 'Dholpur']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Hubballi vs Ballabgarh vs Dholpur – 2019', width=550, height=320)
     return chart


</code>
"
3395,temporal_aggregation,Show the monthly average PM2.5 for Nagapattinam in 2017 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Nagapattinam') & (data['Timestamp'].dt.year == 2017)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Nagapattinam 2017', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Nagapattinam') & (data['Timestamp'].dt.year == 2017)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Nagapattinam 2017', width=450, height=280)


</code>
"
3396,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Delhi, Meghalaya, and Karnataka in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Delhi', 'Meghalaya', 'Karnataka']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Delhi, Meghalaya, UP – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Delhi', 'Meghalaya', 'Karnataka']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Delhi, Meghalaya, UP – 2023', width=550, height=320)
     return chart


</code>
"
3397,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Hubballi, Darbhanga, and Ramanagara in 2019 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Hubballi', 'Darbhanga', 'Ramanagara']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Hubballi vs Darbhanga vs Ramanagara – 2019', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Hubballi', 'Darbhanga', 'Ramanagara']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Hubballi vs Darbhanga vs Ramanagara – 2019', width=550, height=320)
     return chart


</code>
"
3398,temporal_aggregation,Show the monthly average PM10 trend for Howrah from 2017 to 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Howrah'].copy()
    df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Howrah (2017–2022)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Howrah'].copy()
     df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Howrah (2017–2022)', width=600, height=300)
     return chart


</code>
"
3399,temporal_aggregation,Show the monthly average PM2.5 for Shillong in 2024 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Shillong') & (data['Timestamp'].dt.year == 2024)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Shillong 2024', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Shillong') & (data['Timestamp'].dt.year == 2024)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Shillong 2024', width=450, height=280)


</code>
"
3400,specific_pattern,Show a cumulative area chart of PM2.5 readings for Bulandshahr across 2024.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Bulandshahr') & (data['Timestamp'].dt.year == 2024)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Bulandshahr 2024', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Bulandshahr') & (data['Timestamp'].dt.year == 2024)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Bulandshahr 2024', width=600, height=300)
     return chart


</code>
"
3401,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Chhattisgarh, Assam, and Himachal Pradesh in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Chhattisgarh', 'Assam', 'Himachal Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Chhattisgarh, Assam, UP – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Chhattisgarh', 'Assam', 'Himachal Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Chhattisgarh, Assam, UP – 2023', width=550, height=320)
     return chart


</code>
"
3402,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Odisha, Uttarakhand, and Kerala in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Odisha', 'Uttarakhand', 'Kerala']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Odisha, Uttarakhand, UP – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Odisha', 'Uttarakhand', 'Kerala']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Odisha, Uttarakhand, UP – 2024', width=550, height=320)
     return chart


</code>
"
3403,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Haryana, Meghalaya, and Uttarakhand from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Haryana', 'Meghalaya', 'Uttarakhand']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Haryana vs Meghalaya vs Uttarakhand', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Haryana', 'Meghalaya', 'Uttarakhand']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Haryana vs Meghalaya vs Uttarakhand', width=550, height=320)
     return chart


</code>
"
3404,temporal_aggregation,Show the monthly average PM10 trend for Sasaram from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Sasaram'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Sasaram (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Sasaram'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Sasaram (2019–2024)', width=600, height=300)
     return chart


</code>
"
3405,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Jharkhand stations in 2023, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Jharkhand') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Jharkhand Stations 2023', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Jharkhand') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Jharkhand Stations 2023', width=450, height=350)


</code>
"
3406,temporal_aggregation,Show the monthly average PM10 trend for Dharuhera from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Dharuhera'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Dharuhera (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Dharuhera'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Dharuhera (2019–2024)', width=600, height=300)
     return chart


</code>
"
3407,specific_pattern,Show a cumulative area chart of PM2.5 readings for Chikkamagaluru across 2024.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Chikkamagaluru') & (data['Timestamp'].dt.year == 2024)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Chikkamagaluru 2024', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Chikkamagaluru') & (data['Timestamp'].dt.year == 2024)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Chikkamagaluru 2024', width=600, height=300)
     return chart


</code>
"
3408,temporal_aggregation,Show the monthly average PM10 trend for Hapur from 2017 to 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Hapur'].copy()
    df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Hapur (2017–2022)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Hapur'].copy()
     df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Hapur (2017–2022)', width=600, height=300)
     return chart


</code>
"
3409,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Kerala stations in 2023, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Kerala') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Kerala Stations 2023', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Kerala') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Kerala Stations 2023', width=450, height=350)


</code>
"
3410,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Andhra Pradesh, Nagaland, and Tamil Nadu in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Andhra Pradesh', 'Nagaland', 'Tamil Nadu']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Andhra Pradesh, Nagaland, UP – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Andhra Pradesh', 'Nagaland', 'Tamil Nadu']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Andhra Pradesh, Nagaland, UP – 2024', width=550, height=320)
     return chart


</code>
"
3411,temporal_aggregation,Show a monthly bar chart of the number of days Kerala exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2018.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Kerala') & (data['Timestamp'].dt.year == 2018)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Kerala Exceeded WHO PM2.5 Guideline per Month – 2018', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Kerala') & (data['Timestamp'].dt.year == 2018)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Kerala Exceeded WHO PM2.5 Guideline per Month – 2018', width=500, height=300)
     return chart


</code>
"
3412,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Telangana, Kerala, and Madhya Pradesh across 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Telangana', 'Kerala', 'Madhya Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Telangana', 'Kerala', 'Madhya Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
     return chart


</code>
"
3413,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Punjab, Rajasthan, and Jammu and Kashmir in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Punjab', 'Rajasthan', 'Jammu and Kashmir']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Punjab, Rajasthan, UP – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Punjab', 'Rajasthan', 'Jammu and Kashmir']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Punjab, Rajasthan, UP – 2024', width=550, height=320)
     return chart


</code>
"
3414,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Nagaland, Karnataka, and Maharashtra in 2017 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Nagaland', 'Karnataka', 'Maharashtra']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2017)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Nagaland, Karnataka, UP – 2017', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Nagaland', 'Karnataka', 'Maharashtra']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2017)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Nagaland, Karnataka, UP – 2017', width=550, height=320)
     return chart


</code>
"
3415,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for West Bengal, Tamil Nadu, and Jharkhand across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['West Bengal', 'Tamil Nadu', 'Jharkhand']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['West Bengal', 'Tamil Nadu', 'Jharkhand']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
3416,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Nagaland, Himachal Pradesh, and Delhi from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Nagaland', 'Himachal Pradesh', 'Delhi']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Nagaland vs Himachal Pradesh vs Delhi', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Nagaland', 'Himachal Pradesh', 'Delhi']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Nagaland vs Himachal Pradesh vs Delhi', width=550, height=320)
     return chart


</code>
"
3417,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Puducherry, Karnataka, and Mizoram from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Puducherry', 'Karnataka', 'Mizoram']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Puducherry vs Karnataka vs Mizoram', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Puducherry', 'Karnataka', 'Mizoram']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Puducherry vs Karnataka vs Mizoram', width=550, height=320)
     return chart


</code>
"
3418,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Jammu and Kashmir, Delhi, and Madhya Pradesh from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Jammu and Kashmir', 'Delhi', 'Madhya Pradesh']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Jammu and Kashmir vs Delhi vs Madhya Pradesh', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Jammu and Kashmir', 'Delhi', 'Madhya Pradesh']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Jammu and Kashmir vs Delhi vs Madhya Pradesh', width=550, height=320)
     return chart


</code>
"
3419,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Telangana, Tamil Nadu, and Manipur in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Telangana', 'Tamil Nadu', 'Manipur']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Telangana, Tamil Nadu, UP – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Telangana', 'Tamil Nadu', 'Manipur']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Telangana, Tamil Nadu, UP – 2024', width=550, height=320)
     return chart


</code>
"
3420,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Maharashtra, Uttarakhand, and Gujarat in 2017 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Maharashtra', 'Uttarakhand', 'Gujarat']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2017)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Maharashtra, Uttarakhand, UP – 2017', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Maharashtra', 'Uttarakhand', 'Gujarat']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2017)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Maharashtra, Uttarakhand, UP – 2017', width=550, height=320)
     return chart


</code>
"
3421,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Telangana, Puducherry, and Puducherry in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Telangana', 'Puducherry', 'Puducherry']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Telangana, Puducherry, UP – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Telangana', 'Puducherry', 'Puducherry']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Telangana, Puducherry, UP – 2023', width=550, height=320)
     return chart


</code>
"
3422,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Himachal Pradesh, Bihar, and Himachal Pradesh from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Himachal Pradesh', 'Bihar', 'Himachal Pradesh']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Himachal Pradesh vs Bihar vs Himachal Pradesh', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Himachal Pradesh', 'Bihar', 'Himachal Pradesh']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Himachal Pradesh vs Bihar vs Himachal Pradesh', width=550, height=320)
     return chart


</code>
"
3423,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Jharkhand, Jammu and Kashmir, and Arunachal Pradesh from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Jharkhand', 'Jammu and Kashmir', 'Arunachal Pradesh']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Jharkhand vs Jammu and Kashmir vs Arunachal Pradesh', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Jharkhand', 'Jammu and Kashmir', 'Arunachal Pradesh']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Jharkhand vs Jammu and Kashmir vs Arunachal Pradesh', width=550, height=320)
     return chart


</code>
"
3424,specific_pattern,Plot the rolling 30-day average PM2.5 for Himachal Pradesh in 2023 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Himachal Pradesh') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Himachal Pradesh 2023', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Himachal Pradesh') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Himachal Pradesh 2023', width=600, height=300)


</code>
"
3425,temporal_aggregation,Show a monthly bar chart of the number of days Telangana exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2020.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Telangana') & (data['Timestamp'].dt.year == 2020)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Telangana Exceeded WHO PM2.5 Guideline per Month – 2020', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Telangana') & (data['Timestamp'].dt.year == 2020)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Telangana Exceeded WHO PM2.5 Guideline per Month – 2020', width=500, height=300)
     return chart


</code>
"
3426,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Jharkhand, Rajasthan, and Jharkhand in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Jharkhand', 'Rajasthan', 'Jharkhand']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Jharkhand, Rajasthan, UP – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Jharkhand', 'Rajasthan', 'Jharkhand']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Jharkhand, Rajasthan, UP – 2024', width=550, height=320)
     return chart


</code>
"
3427,specific_pattern,Show a cumulative area chart of PM2.5 readings for Kota across 2021.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Kota') & (data['Timestamp'].dt.year == 2021)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Kota 2021', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Kota') & (data['Timestamp'].dt.year == 2021)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Kota 2021', width=600, height=300)
     return chart


</code>
"
3428,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Chhattisgarh, Manipur, and West Bengal from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Chhattisgarh', 'Manipur', 'West Bengal']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Chhattisgarh vs Manipur vs West Bengal', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Chhattisgarh', 'Manipur', 'West Bengal']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Chhattisgarh vs Manipur vs West Bengal', width=550, height=320)
     return chart


</code>
"
3429,temporal_aggregation,Show the monthly average PM10 trend for Parbhani from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Parbhani'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Parbhani (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Parbhani'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Parbhani (2019–2024)', width=600, height=300)
     return chart


</code>
"
3430,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Tamil Nadu, Telangana, and Kerala in 2017 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Tamil Nadu', 'Telangana', 'Kerala']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2017)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Tamil Nadu, Telangana, UP – 2017', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Tamil Nadu', 'Telangana', 'Kerala']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2017)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Tamil Nadu, Telangana, UP – 2017', width=550, height=320)
     return chart


</code>
"
3431,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Jammu and Kashmir, Andhra Pradesh, and Chandigarh across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Jammu and Kashmir', 'Andhra Pradesh', 'Chandigarh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Jammu and Kashmir', 'Andhra Pradesh', 'Chandigarh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
3432,temporal_aggregation,Plot the weekly average PM2.5 for Pratapgarh in 2023 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Pratapgarh') & (data['Timestamp'].dt.year == 2023)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Pratapgarh 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Pratapgarh') & (data['Timestamp'].dt.year == 2023)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Pratapgarh 2023', width=600, height=300)
     return chart


</code>
"
3433,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Rajasthan, Sikkim, and Punjab from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Rajasthan', 'Sikkim', 'Punjab']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Rajasthan vs Sikkim vs Punjab', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Rajasthan', 'Sikkim', 'Punjab']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Rajasthan vs Sikkim vs Punjab', width=550, height=320)
     return chart


</code>
"
3434,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Gummidipoondi, Vatva, and Kashipur in 2019 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Gummidipoondi', 'Vatva', 'Kashipur']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Gummidipoondi vs Vatva vs Kashipur – 2019', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Gummidipoondi', 'Vatva', 'Kashipur']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Gummidipoondi vs Vatva vs Kashipur – 2019', width=550, height=320)
     return chart


</code>
"
3435,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Nalbari, Raichur, and Asansol in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Nalbari', 'Raichur', 'Asansol']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Nalbari vs Raichur vs Asansol – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Nalbari', 'Raichur', 'Asansol']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Nalbari vs Raichur vs Asansol – 2022', width=550, height=320)
     return chart


</code>
"
3436,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Sikkim, Delhi, and Tamil Nadu across 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Sikkim', 'Delhi', 'Tamil Nadu']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Sikkim', 'Delhi', 'Tamil Nadu']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
     return chart


</code>
"
3437,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Punjab, Maharashtra, and Tamil Nadu in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Punjab', 'Maharashtra', 'Tamil Nadu']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Punjab, Maharashtra, UP – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Punjab', 'Maharashtra', 'Tamil Nadu']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Punjab, Maharashtra, UP – 2024', width=550, height=320)
     return chart


</code>
"
3438,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Delhi, Puducherry, and Bihar in 2017 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Delhi', 'Puducherry', 'Bihar']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2017)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Delhi, Puducherry, UP – 2017', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Delhi', 'Puducherry', 'Bihar']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2017)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Delhi, Puducherry, UP – 2017', width=550, height=320)
     return chart


</code>
"
3439,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Nagaur, Chikkamagaluru, and Barbil in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Nagaur', 'Chikkamagaluru', 'Barbil']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Nagaur vs Chikkamagaluru vs Barbil – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Nagaur', 'Chikkamagaluru', 'Barbil']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Nagaur vs Chikkamagaluru vs Barbil – 2023', width=550, height=320)
     return chart


</code>
"
3440,temporal_aggregation,Plot the weekly average PM2.5 for Siwan in 2021 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Siwan') & (data['Timestamp'].dt.year == 2021)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Siwan 2021', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Siwan') & (data['Timestamp'].dt.year == 2021)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Siwan 2021', width=600, height=300)
     return chart


</code>
"
3441,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Sikkim, Gujarat, and Puducherry in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Sikkim', 'Gujarat', 'Puducherry']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Sikkim, Gujarat, UP – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Sikkim', 'Gujarat', 'Puducherry']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Sikkim, Gujarat, UP – 2022', width=550, height=320)
     return chart


</code>
"
3442,temporal_aggregation,Show the monthly average PM2.5 for Bikaner in 2019 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Bikaner') & (data['Timestamp'].dt.year == 2019)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Bikaner 2019', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Bikaner') & (data['Timestamp'].dt.year == 2019)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Bikaner 2019', width=450, height=280)


</code>
"
3443,temporal_aggregation,Show the monthly average PM2.5 for Bhilai in 2020 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Bhilai') & (data['Timestamp'].dt.year == 2020)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Bhilai 2020', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Bhilai') & (data['Timestamp'].dt.year == 2020)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Bhilai 2020', width=450, height=280)


</code>
"
3444,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Gadag, Noida, and Vapi in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Gadag', 'Noida', 'Vapi']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Gadag vs Noida vs Vapi – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Gadag', 'Noida', 'Vapi']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Gadag vs Noida vs Vapi – 2022', width=550, height=320)
     return chart


</code>
"
3445,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Himachal Pradesh, Assam, and Jharkhand in 2021 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Himachal Pradesh', 'Assam', 'Jharkhand']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Himachal Pradesh, Assam, UP – 2021', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Himachal Pradesh', 'Assam', 'Jharkhand']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Himachal Pradesh, Assam, UP – 2021', width=550, height=320)
     return chart


</code>
"
3446,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Parbhani, Jalore, and Chandigarh in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Parbhani', 'Jalore', 'Chandigarh']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Parbhani vs Jalore vs Chandigarh – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Parbhani', 'Jalore', 'Chandigarh']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Parbhani vs Jalore vs Chandigarh – 2024', width=550, height=320)
     return chart


</code>
"
3447,specific_pattern,Plot the rolling 30-day average PM2.5 for Chandigarh in 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Chandigarh') & (data['Timestamp'].dt.year == 2022)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Chandigarh 2022', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Chandigarh') & (data['Timestamp'].dt.year == 2022)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Chandigarh 2022', width=600, height=300)


</code>
"
3448,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Agra, Ranipet, and Tirunelveli in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Agra', 'Ranipet', 'Tirunelveli']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Agra vs Ranipet vs Tirunelveli – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Agra', 'Ranipet', 'Tirunelveli']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Agra vs Ranipet vs Tirunelveli – 2022', width=550, height=320)
     return chart


</code>
"
3449,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Punjab, Bihar, and Gujarat in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Punjab', 'Bihar', 'Gujarat']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Punjab, Bihar, UP – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Punjab', 'Bihar', 'Gujarat']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Punjab, Bihar, UP – 2022', width=550, height=320)
     return chart


</code>
"
3450,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Chhattisgarh, Sikkim, and Kerala from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Chhattisgarh', 'Sikkim', 'Kerala']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Chhattisgarh vs Sikkim vs Kerala', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Chhattisgarh', 'Sikkim', 'Kerala']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Chhattisgarh vs Sikkim vs Kerala', width=550, height=320)
     return chart


</code>
"
3451,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for West Bengal, Punjab, and Assam from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['West Bengal', 'Punjab', 'Assam']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: West Bengal vs Punjab vs Assam', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['West Bengal', 'Punjab', 'Assam']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: West Bengal vs Punjab vs Assam', width=550, height=320)
     return chart


</code>
"
3452,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Telangana, Puducherry, and Arunachal Pradesh in 2017 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Telangana', 'Puducherry', 'Arunachal Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2017)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Telangana, Puducherry, UP – 2017', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Telangana', 'Puducherry', 'Arunachal Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2017)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Telangana, Puducherry, UP – 2017', width=550, height=320)
     return chart


</code>
"
3453,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Akola, Samastipur, and Begusarai in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Akola', 'Samastipur', 'Begusarai']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Akola vs Samastipur vs Begusarai – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Akola', 'Samastipur', 'Begusarai']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Akola vs Samastipur vs Begusarai – 2022', width=550, height=320)
     return chart


</code>
"
3454,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Bihar, Tamil Nadu, and Andhra Pradesh in 2021 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Bihar', 'Tamil Nadu', 'Andhra Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Bihar, Tamil Nadu, UP – 2021', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Bihar', 'Tamil Nadu', 'Andhra Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Bihar, Tamil Nadu, UP – 2021', width=550, height=320)
     return chart


</code>
"
3455,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Odisha, Jammu and Kashmir, and Jammu and Kashmir from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Odisha', 'Jammu and Kashmir', 'Jammu and Kashmir']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Odisha vs Jammu and Kashmir vs Jammu and Kashmir', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Odisha', 'Jammu and Kashmir', 'Jammu and Kashmir']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Odisha vs Jammu and Kashmir vs Jammu and Kashmir', width=550, height=320)
     return chart


</code>
"
3456,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Arunachal Pradesh, Nagaland, and Haryana across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Arunachal Pradesh', 'Nagaland', 'Haryana']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Arunachal Pradesh', 'Nagaland', 'Haryana']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
3457,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Gujarat, Manipur, and Puducherry from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Gujarat', 'Manipur', 'Puducherry']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Gujarat vs Manipur vs Puducherry', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Gujarat', 'Manipur', 'Puducherry']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Gujarat vs Manipur vs Puducherry', width=550, height=320)
     return chart


</code>
"
3458,temporal_aggregation,Plot the weekly average PM2.5 for Imphal in 2023 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Imphal') & (data['Timestamp'].dt.year == 2023)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Imphal 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Imphal') & (data['Timestamp'].dt.year == 2023)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Imphal 2023', width=600, height=300)
     return chart


</code>
"
3459,temporal_aggregation,Plot the weekly average PM2.5 for Narnaul in 2023 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Narnaul') & (data['Timestamp'].dt.year == 2023)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Narnaul 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Narnaul') & (data['Timestamp'].dt.year == 2023)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Narnaul 2023', width=600, height=300)
     return chart


</code>
"
3460,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Haryana, Maharashtra, and Manipur across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Haryana', 'Maharashtra', 'Manipur']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Haryana', 'Maharashtra', 'Manipur']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
3461,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Chandigarh, Nagaland, and Tripura in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Chandigarh', 'Nagaland', 'Tripura']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Chandigarh, Nagaland, UP – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Chandigarh', 'Nagaland', 'Tripura']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Chandigarh, Nagaland, UP – 2024', width=550, height=320)
     return chart


</code>
"
3462,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Kanpur, Jaisalmer, and Bhagalpur in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Kanpur', 'Jaisalmer', 'Bhagalpur']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Kanpur vs Jaisalmer vs Bhagalpur – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Kanpur', 'Jaisalmer', 'Bhagalpur']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Kanpur vs Jaisalmer vs Bhagalpur – 2023', width=550, height=320)
     return chart


</code>
"
3463,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Telangana, Madhya Pradesh, and Jammu and Kashmir from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Telangana', 'Madhya Pradesh', 'Jammu and Kashmir']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Telangana vs Madhya Pradesh vs Jammu and Kashmir', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Telangana', 'Madhya Pradesh', 'Jammu and Kashmir']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Telangana vs Madhya Pradesh vs Jammu and Kashmir', width=550, height=320)
     return chart


</code>
"
3464,temporal_aggregation,Show the monthly average PM10 trend for Anantapur from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Anantapur'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Anantapur (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Anantapur'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Anantapur (2019–2024)', width=600, height=300)
     return chart


</code>
"
3465,specific_pattern,Show a cumulative area chart of PM2.5 readings for Ramanathapuram across 2022.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Ramanathapuram') & (data['Timestamp'].dt.year == 2022)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Ramanathapuram 2022', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Ramanathapuram') & (data['Timestamp'].dt.year == 2022)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Ramanathapuram 2022', width=600, height=300)
     return chart


</code>
"
3466,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Nagaland, Gujarat, and Punjab from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Nagaland', 'Gujarat', 'Punjab']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Nagaland vs Gujarat vs Punjab', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Nagaland', 'Gujarat', 'Punjab']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Nagaland vs Gujarat vs Punjab', width=550, height=320)
     return chart


</code>
"
3467,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Bihar Sharif, Sawai Madhopur, and Chhal in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Bihar Sharif', 'Sawai Madhopur', 'Chhal']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Bihar Sharif vs Sawai Madhopur vs Chhal – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Bihar Sharif', 'Sawai Madhopur', 'Chhal']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Bihar Sharif vs Sawai Madhopur vs Chhal – 2023', width=550, height=320)
     return chart


</code>
"
3468,temporal_aggregation,Show the monthly average PM2.5 for Jodhpur in 2022 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Jodhpur') & (data['Timestamp'].dt.year == 2022)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Jodhpur 2022', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Jodhpur') & (data['Timestamp'].dt.year == 2022)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Jodhpur 2022', width=450, height=280)


</code>
"
3469,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Chittoor, Bharatpur, and Thanjavur in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Chittoor', 'Bharatpur', 'Thanjavur']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Chittoor vs Bharatpur vs Thanjavur – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Chittoor', 'Bharatpur', 'Thanjavur']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Chittoor vs Bharatpur vs Thanjavur – 2024', width=550, height=320)
     return chart


</code>
"
3470,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Andhra Pradesh, Puducherry, and Arunachal Pradesh from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Andhra Pradesh', 'Puducherry', 'Arunachal Pradesh']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Andhra Pradesh vs Puducherry vs Arunachal Pradesh', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Andhra Pradesh', 'Puducherry', 'Arunachal Pradesh']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Andhra Pradesh vs Puducherry vs Arunachal Pradesh', width=550, height=320)
     return chart


</code>
"
3471,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Tripura, Kerala, and Uttar Pradesh in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Tripura', 'Kerala', 'Uttar Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Tripura, Kerala, UP – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Tripura', 'Kerala', 'Uttar Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Tripura, Kerala, UP – 2023', width=550, height=320)
     return chart


</code>
"
3472,temporal_aggregation,Plot the weekly average PM2.5 for Bharatpur in 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Bharatpur') & (data['Timestamp'].dt.year == 2024)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Bharatpur 2024', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Bharatpur') & (data['Timestamp'].dt.year == 2024)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Bharatpur 2024', width=600, height=300)
     return chart


</code>
"
3473,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Telangana, Chandigarh, and Andhra Pradesh from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Telangana', 'Chandigarh', 'Andhra Pradesh']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Telangana vs Chandigarh vs Andhra Pradesh', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Telangana', 'Chandigarh', 'Andhra Pradesh']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Telangana vs Chandigarh vs Andhra Pradesh', width=550, height=320)
     return chart


</code>
"
3474,temporal_aggregation,Show the monthly average PM2.5 for Kadapa in 2020 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Kadapa') & (data['Timestamp'].dt.year == 2020)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Kadapa 2020', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Kadapa') & (data['Timestamp'].dt.year == 2020)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Kadapa 2020', width=450, height=280)


</code>
"
3475,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Jammu and Kashmir, Odisha, and Uttarakhand across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Jammu and Kashmir', 'Odisha', 'Uttarakhand']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Jammu and Kashmir', 'Odisha', 'Uttarakhand']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
3476,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Andhra Pradesh, Gujarat, and Telangana across 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Andhra Pradesh', 'Gujarat', 'Telangana']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Andhra Pradesh', 'Gujarat', 'Telangana']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
     return chart


</code>
"
3477,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Tamil Nadu, Puducherry, and Mizoram from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Tamil Nadu', 'Puducherry', 'Mizoram']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Tamil Nadu vs Puducherry vs Mizoram', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Tamil Nadu', 'Puducherry', 'Mizoram']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Tamil Nadu vs Puducherry vs Mizoram', width=550, height=320)
     return chart


</code>
"
3478,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Puducherry, Jammu and Kashmir, and Chhattisgarh from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Puducherry', 'Jammu and Kashmir', 'Chhattisgarh']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Puducherry vs Jammu and Kashmir vs Chhattisgarh', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Puducherry', 'Jammu and Kashmir', 'Chhattisgarh']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Puducherry vs Jammu and Kashmir vs Chhattisgarh', width=550, height=320)
     return chart


</code>
"
3479,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Haryana, Mizoram, and Nagaland in 2021 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Haryana', 'Mizoram', 'Nagaland']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Haryana, Mizoram, UP – 2021', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Haryana', 'Mizoram', 'Nagaland']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Haryana, Mizoram, UP – 2021', width=550, height=320)
     return chart


</code>
"
3480,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Karnal, Thrissur, and Kolar in 2019 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Karnal', 'Thrissur', 'Kolar']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Karnal vs Thrissur vs Kolar – 2019', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Karnal', 'Thrissur', 'Kolar']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Karnal vs Thrissur vs Kolar – 2019', width=550, height=320)
     return chart


</code>
"
3481,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Rajasthan, Uttarakhand, and Odisha in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Rajasthan', 'Uttarakhand', 'Odisha']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Rajasthan, Uttarakhand, UP – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Rajasthan', 'Uttarakhand', 'Odisha']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Rajasthan, Uttarakhand, UP – 2024', width=550, height=320)
     return chart


</code>
"
3482,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Manipur, Tripura, and Bihar across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Manipur', 'Tripura', 'Bihar']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Manipur', 'Tripura', 'Bihar']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
3483,temporal_aggregation,Show a monthly bar chart of the number of days Maharashtra exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2024.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Maharashtra') & (data['Timestamp'].dt.year == 2024)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Maharashtra Exceeded WHO PM2.5 Guideline per Month – 2024', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Maharashtra') & (data['Timestamp'].dt.year == 2024)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Maharashtra Exceeded WHO PM2.5 Guideline per Month – 2024', width=500, height=300)
     return chart


</code>
"
3484,temporal_aggregation,Show the monthly average PM2.5 for Talcher in 2019 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Talcher') & (data['Timestamp'].dt.year == 2019)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Talcher 2019', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Talcher') & (data['Timestamp'].dt.year == 2019)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Talcher 2019', width=450, height=280)


</code>
"
3485,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Rajasthan, Uttarakhand, and Madhya Pradesh from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Rajasthan', 'Uttarakhand', 'Madhya Pradesh']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Rajasthan vs Uttarakhand vs Madhya Pradesh', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Rajasthan', 'Uttarakhand', 'Madhya Pradesh']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Rajasthan vs Uttarakhand vs Madhya Pradesh', width=550, height=320)
     return chart


</code>
"
3486,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Bhilwara, Kalyan, and Ratlam in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Bhilwara', 'Kalyan', 'Ratlam']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Bhilwara vs Kalyan vs Ratlam – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Bhilwara', 'Kalyan', 'Ratlam']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Bhilwara vs Kalyan vs Ratlam – 2023', width=550, height=320)
     return chart


</code>
"
3487,specific_pattern,Plot the rolling 30-day average PM2.5 for Karnataka in 2018 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Karnataka') & (data['Timestamp'].dt.year == 2018)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Karnataka 2018', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Karnataka') & (data['Timestamp'].dt.year == 2018)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Karnataka 2018', width=600, height=300)


</code>
"
3488,temporal_aggregation,Plot the weekly average PM2.5 for Tirupur in 2023 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Tirupur') & (data['Timestamp'].dt.year == 2023)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Tirupur 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Tirupur') & (data['Timestamp'].dt.year == 2023)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Tirupur 2023', width=600, height=300)
     return chart


</code>
"
3489,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for West Bengal, Karnataka, and Jharkhand across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['West Bengal', 'Karnataka', 'Jharkhand']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['West Bengal', 'Karnataka', 'Jharkhand']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
3490,temporal_aggregation,Show the monthly average PM2.5 for Dholpur in 2017 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Dholpur') & (data['Timestamp'].dt.year == 2017)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Dholpur 2017', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Dholpur') & (data['Timestamp'].dt.year == 2017)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Dholpur 2017', width=450, height=280)


</code>
"
3491,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Punjab, Jharkhand, and Kerala from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Punjab', 'Jharkhand', 'Kerala']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Punjab vs Jharkhand vs Kerala', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Punjab', 'Jharkhand', 'Kerala']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Punjab vs Jharkhand vs Kerala', width=550, height=320)
     return chart


</code>
"
3492,specific_pattern,Show a cumulative area chart of PM2.5 readings for Gwalior across 2021.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Gwalior') & (data['Timestamp'].dt.year == 2021)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Gwalior 2021', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Gwalior') & (data['Timestamp'].dt.year == 2021)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Gwalior 2021', width=600, height=300)
     return chart


</code>
"
3493,specific_pattern,Plot the rolling 30-day average PM2.5 for Andhra Pradesh in 2023 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Andhra Pradesh') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Andhra Pradesh 2023', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Andhra Pradesh') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Andhra Pradesh 2023', width=600, height=300)


</code>
"
3494,specific_pattern,Show a cumulative area chart of PM2.5 readings for Jind across 2022.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Jind') & (data['Timestamp'].dt.year == 2022)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Jind 2022', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Jind') & (data['Timestamp'].dt.year == 2022)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Jind 2022', width=600, height=300)
     return chart


</code>
"
3495,temporal_aggregation,Plot the weekly average PM2.5 for Bahadurgarh in 2023 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Bahadurgarh') & (data['Timestamp'].dt.year == 2023)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Bahadurgarh 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Bahadurgarh') & (data['Timestamp'].dt.year == 2023)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Bahadurgarh 2023', width=600, height=300)
     return chart


</code>
"
3496,specific_pattern,Plot the rolling 30-day average PM2.5 for Uttar Pradesh in 2018 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Uttar Pradesh') & (data['Timestamp'].dt.year == 2018)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Uttar Pradesh 2018', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Uttar Pradesh') & (data['Timestamp'].dt.year == 2018)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Uttar Pradesh 2018', width=600, height=300)


</code>
"
3497,temporal_aggregation,Show the monthly average PM2.5 for Madurai in 2020 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Madurai') & (data['Timestamp'].dt.year == 2020)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Madurai 2020', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Madurai') & (data['Timestamp'].dt.year == 2020)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Madurai 2020', width=450, height=280)


</code>
"
3498,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Tripura, Meghalaya, and West Bengal across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Tripura', 'Meghalaya', 'West Bengal']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Tripura', 'Meghalaya', 'West Bengal']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
3499,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Bihar, Telangana, and Kerala from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Bihar', 'Telangana', 'Kerala']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Bihar vs Telangana vs Kerala', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Bihar', 'Telangana', 'Kerala']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Bihar vs Telangana vs Kerala', width=550, height=320)
     return chart


</code>
"
3500,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Telangana, Maharashtra, and Meghalaya across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Telangana', 'Maharashtra', 'Meghalaya']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Telangana', 'Maharashtra', 'Meghalaya']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
3501,specific_pattern,Plot the rolling 30-day average PM2.5 for Andhra Pradesh in 2019 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Andhra Pradesh') & (data['Timestamp'].dt.year == 2019)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Andhra Pradesh 2019', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Andhra Pradesh') & (data['Timestamp'].dt.year == 2019)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Andhra Pradesh 2019', width=600, height=300)


</code>
"
3502,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Andhra Pradesh, Tripura, and Haryana in 2018 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Andhra Pradesh', 'Tripura', 'Haryana']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2018)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Andhra Pradesh, Tripura, UP – 2018', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Andhra Pradesh', 'Tripura', 'Haryana']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2018)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Andhra Pradesh, Tripura, UP – 2018', width=550, height=320)
     return chart


</code>
"
3503,specific_pattern,Show a cumulative area chart of PM2.5 readings for Thiruvananthapuram across 2018.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Thiruvananthapuram') & (data['Timestamp'].dt.year == 2018)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Thiruvananthapuram 2018', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Thiruvananthapuram') & (data['Timestamp'].dt.year == 2018)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Thiruvananthapuram 2018', width=600, height=300)
     return chart


</code>
"
3504,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Rajasthan stations in 2017, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Rajasthan') & (data['Timestamp'].dt.year == 2017)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Rajasthan Stations 2017', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Rajasthan') & (data['Timestamp'].dt.year == 2017)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Rajasthan Stations 2017', width=450, height=350)


</code>
"
3505,temporal_aggregation,Show the monthly average PM2.5 for Madikeri in 2017 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Madikeri') & (data['Timestamp'].dt.year == 2017)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Madikeri 2017', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Madikeri') & (data['Timestamp'].dt.year == 2017)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Madikeri 2017', width=450, height=280)


</code>
"
3506,temporal_aggregation,Show a monthly bar chart of the number of days Jammu and Kashmir exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2023.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Jammu and Kashmir') & (data['Timestamp'].dt.year == 2023)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Jammu and Kashmir Exceeded WHO PM2.5 Guideline per Month – 2023', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Jammu and Kashmir') & (data['Timestamp'].dt.year == 2023)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Jammu and Kashmir Exceeded WHO PM2.5 Guideline per Month – 2023', width=500, height=300)
     return chart


</code>
"
3507,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Haryana, Uttarakhand, and Rajasthan in 2019 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Haryana', 'Uttarakhand', 'Rajasthan']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Haryana, Uttarakhand, UP – 2019', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Haryana', 'Uttarakhand', 'Rajasthan']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Haryana, Uttarakhand, UP – 2019', width=550, height=320)
     return chart


</code>
"
3508,specific_pattern,Plot the rolling 30-day average PM2.5 for Nagaland in 2018 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Nagaland') & (data['Timestamp'].dt.year == 2018)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Nagaland 2018', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Nagaland') & (data['Timestamp'].dt.year == 2018)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Nagaland 2018', width=600, height=300)


</code>
"
3509,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Assam, Haryana, and Madhya Pradesh from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Assam', 'Haryana', 'Madhya Pradesh']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Assam vs Haryana vs Madhya Pradesh', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Assam', 'Haryana', 'Madhya Pradesh']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Assam vs Haryana vs Madhya Pradesh', width=550, height=320)
     return chart


</code>
"
3510,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Uttarakhand, Haryana, and West Bengal in 2017 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Uttarakhand', 'Haryana', 'West Bengal']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2017)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Uttarakhand, Haryana, UP – 2017', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Uttarakhand', 'Haryana', 'West Bengal']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2017)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Uttarakhand, Haryana, UP – 2017', width=550, height=320)
     return chart


</code>
"
3511,specific_pattern,Show a cumulative area chart of PM2.5 readings for Kishanganj across 2024.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Kishanganj') & (data['Timestamp'].dt.year == 2024)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Kishanganj 2024', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Kishanganj') & (data['Timestamp'].dt.year == 2024)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Kishanganj 2024', width=600, height=300)
     return chart


</code>
"
3512,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Madhya Pradesh stations in 2018, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Madhya Pradesh') & (data['Timestamp'].dt.year == 2018)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Madhya Pradesh Stations 2018', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Madhya Pradesh') & (data['Timestamp'].dt.year == 2018)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Madhya Pradesh Stations 2018', width=450, height=350)


</code>
"
3513,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Navi Mumbai, Bhiwadi, and Bileipada in 2019 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Navi Mumbai', 'Bhiwadi', 'Bileipada']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Navi Mumbai vs Bhiwadi vs Bileipada – 2019', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Navi Mumbai', 'Bhiwadi', 'Bileipada']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Navi Mumbai vs Bhiwadi vs Bileipada – 2019', width=550, height=320)
     return chart


</code>
"
3514,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Chandigarh, Manipur, and Puducherry across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Chandigarh', 'Manipur', 'Puducherry']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Chandigarh', 'Manipur', 'Puducherry']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
3515,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Andhra Pradesh, Uttarakhand, and Puducherry from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Andhra Pradesh', 'Uttarakhand', 'Puducherry']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Andhra Pradesh vs Uttarakhand vs Puducherry', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Andhra Pradesh', 'Uttarakhand', 'Puducherry']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Andhra Pradesh vs Uttarakhand vs Puducherry', width=550, height=320)
     return chart


</code>
"
3516,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Mizoram, Telangana, and Nagaland in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Mizoram', 'Telangana', 'Nagaland']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Mizoram, Telangana, UP – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Mizoram', 'Telangana', 'Nagaland']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Mizoram, Telangana, UP – 2023', width=550, height=320)
     return chart


</code>
"
3517,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Delhi, Puducherry, and Manipur from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Delhi', 'Puducherry', 'Manipur']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Delhi vs Puducherry vs Manipur', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Delhi', 'Puducherry', 'Manipur']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Delhi vs Puducherry vs Manipur', width=550, height=320)
     return chart


</code>
"
3518,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Ooty, Samastipur, and Indore in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Ooty', 'Samastipur', 'Indore']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Ooty vs Samastipur vs Indore – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Ooty', 'Samastipur', 'Indore']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Ooty vs Samastipur vs Indore – 2024', width=550, height=320)
     return chart


</code>
"
3519,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Himachal Pradesh, Madhya Pradesh, and Sikkim from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Himachal Pradesh', 'Madhya Pradesh', 'Sikkim']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Himachal Pradesh vs Madhya Pradesh vs Sikkim', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Himachal Pradesh', 'Madhya Pradesh', 'Sikkim']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Himachal Pradesh vs Madhya Pradesh vs Sikkim', width=550, height=320)
     return chart


</code>
"
3520,specific_pattern,Show a cumulative area chart of PM2.5 readings for Araria across 2021.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Araria') & (data['Timestamp'].dt.year == 2021)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Araria 2021', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Araria') & (data['Timestamp'].dt.year == 2021)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Araria 2021', width=600, height=300)
     return chart


</code>
"
3521,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Gwalior, Thanjavur, and Mandideep in 2018 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Gwalior', 'Thanjavur', 'Mandideep']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2018)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Gwalior vs Thanjavur vs Mandideep – 2018', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Gwalior', 'Thanjavur', 'Mandideep']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2018)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Gwalior vs Thanjavur vs Mandideep – 2018', width=550, height=320)
     return chart


</code>
"
3522,temporal_aggregation,Plot the weekly average PM2.5 for Aurangabad in 2019 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Aurangabad') & (data['Timestamp'].dt.year == 2019)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Aurangabad 2019', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Aurangabad') & (data['Timestamp'].dt.year == 2019)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Aurangabad 2019', width=600, height=300)
     return chart


</code>
"
3523,temporal_aggregation,Show the monthly average PM2.5 for Angul in 2019 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Angul') & (data['Timestamp'].dt.year == 2019)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Angul 2019', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Angul') & (data['Timestamp'].dt.year == 2019)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Angul 2019', width=450, height=280)


</code>
"
3524,temporal_aggregation,Show the monthly average PM2.5 for Sri Ganganagar in 2018 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Sri Ganganagar') & (data['Timestamp'].dt.year == 2018)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Sri Ganganagar 2018', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Sri Ganganagar') & (data['Timestamp'].dt.year == 2018)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Sri Ganganagar 2018', width=450, height=280)


</code>
"
3525,temporal_aggregation,Show the monthly average PM10 trend for Bagalkot from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Bagalkot'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Bagalkot (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Bagalkot'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Bagalkot (2019–2024)', width=600, height=300)
     return chart


</code>
"
3526,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Assam, West Bengal, and Sikkim from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Assam', 'West Bengal', 'Sikkim']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Assam vs West Bengal vs Sikkim', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Assam', 'West Bengal', 'Sikkim']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Assam vs West Bengal vs Sikkim', width=550, height=320)
     return chart


</code>
"
3527,temporal_aggregation,Show the monthly average PM10 trend for Kozhikode from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Kozhikode'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Kozhikode (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Kozhikode'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Kozhikode (2019–2024)', width=600, height=300)
     return chart


</code>
"
3528,temporal_aggregation,Show the monthly average PM2.5 for Rourkela in 2018 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Rourkela') & (data['Timestamp'].dt.year == 2018)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Rourkela 2018', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Rourkela') & (data['Timestamp'].dt.year == 2018)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Rourkela 2018', width=450, height=280)


</code>
"
3529,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Chittoor, Firozabad, and Banswara in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Chittoor', 'Firozabad', 'Banswara']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Chittoor vs Firozabad vs Banswara – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Chittoor', 'Firozabad', 'Banswara']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Chittoor vs Firozabad vs Banswara – 2022', width=550, height=320)
     return chart


</code>
"
3530,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Kanpur, Yadgir, and Kanpur in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Kanpur', 'Yadgir', 'Kanpur']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Kanpur vs Yadgir vs Kanpur – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Kanpur', 'Yadgir', 'Kanpur']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Kanpur vs Yadgir vs Kanpur – 2024', width=550, height=320)
     return chart


</code>
"
3531,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Uttarakhand, Andhra Pradesh, and Haryana in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Uttarakhand', 'Andhra Pradesh', 'Haryana']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Uttarakhand, Andhra Pradesh, UP – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Uttarakhand', 'Andhra Pradesh', 'Haryana']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Uttarakhand, Andhra Pradesh, UP – 2023', width=550, height=320)
     return chart


</code>
"
3532,temporal_aggregation,Show the monthly average PM10 trend for Hajipur from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Hajipur'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Hajipur (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Hajipur'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Hajipur (2019–2024)', width=600, height=300)
     return chart


</code>
"
3533,temporal_aggregation,Show the monthly average PM2.5 for Baripada in 2022 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Baripada') & (data['Timestamp'].dt.year == 2022)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Baripada 2022', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Baripada') & (data['Timestamp'].dt.year == 2022)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Baripada 2022', width=450, height=280)


</code>
"
3534,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Ankleshwar, Asansol, and Pathardih in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Ankleshwar', 'Asansol', 'Pathardih']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Ankleshwar vs Asansol vs Pathardih – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Ankleshwar', 'Asansol', 'Pathardih']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Ankleshwar vs Asansol vs Pathardih – 2023', width=550, height=320)
     return chart


</code>
"
3535,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Chhattisgarh, Puducherry, and West Bengal across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Chhattisgarh', 'Puducherry', 'West Bengal']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Chhattisgarh', 'Puducherry', 'West Bengal']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
3536,temporal_aggregation,Show the monthly average PM2.5 for Jabalpur in 2023 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Jabalpur') & (data['Timestamp'].dt.year == 2023)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Jabalpur 2023', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Jabalpur') & (data['Timestamp'].dt.year == 2023)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Jabalpur 2023', width=450, height=280)


</code>
"
3537,temporal_aggregation,Show the monthly average PM10 trend for Tensa from 2017 to 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Tensa'].copy()
    df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Tensa (2017–2022)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Tensa'].copy()
     df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Tensa (2017–2022)', width=600, height=300)
     return chart


</code>
"
3538,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Odisha, Rajasthan, and Madhya Pradesh from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Odisha', 'Rajasthan', 'Madhya Pradesh']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Odisha vs Rajasthan vs Madhya Pradesh', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Odisha', 'Rajasthan', 'Madhya Pradesh']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Odisha vs Rajasthan vs Madhya Pradesh', width=550, height=320)
     return chart


</code>
"
3539,temporal_aggregation,Show the monthly average PM2.5 for Dehradun in 2018 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Dehradun') & (data['Timestamp'].dt.year == 2018)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Dehradun 2018', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Dehradun') & (data['Timestamp'].dt.year == 2018)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Dehradun 2018', width=450, height=280)


</code>
"
3540,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Srinagar, Muzaffarpur, and Pimpri-Chinchwad in 2020 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Srinagar', 'Muzaffarpur', 'Pimpri-Chinchwad']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2020)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Srinagar vs Muzaffarpur vs Pimpri-Chinchwad – 2020', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Srinagar', 'Muzaffarpur', 'Pimpri-Chinchwad']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2020)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Srinagar vs Muzaffarpur vs Pimpri-Chinchwad – 2020', width=550, height=320)
     return chart


</code>
"
3541,temporal_aggregation,Show the monthly average PM10 trend for Sirsa from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Sirsa'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Sirsa (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Sirsa'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Sirsa (2019–2024)', width=600, height=300)
     return chart


</code>
"
3542,specific_pattern,Show a cumulative area chart of PM2.5 readings for Ambala across 2024.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Ambala') & (data['Timestamp'].dt.year == 2024)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Ambala 2024', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Ambala') & (data['Timestamp'].dt.year == 2024)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Ambala 2024', width=600, height=300)
     return chart


</code>
"
3543,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Madhya Pradesh, Punjab, and Chandigarh across 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Madhya Pradesh', 'Punjab', 'Chandigarh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Madhya Pradesh', 'Punjab', 'Chandigarh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
     return chart


</code>
"
3544,temporal_aggregation,Show the monthly average PM2.5 for Sivasagar in 2024 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Sivasagar') & (data['Timestamp'].dt.year == 2024)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Sivasagar 2024', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Sivasagar') & (data['Timestamp'].dt.year == 2024)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Sivasagar 2024', width=450, height=280)


</code>
"
3545,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Jammu and Kashmir, Rajasthan, and Karnataka across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Jammu and Kashmir', 'Rajasthan', 'Karnataka']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Jammu and Kashmir', 'Rajasthan', 'Karnataka']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
3546,temporal_aggregation,Show the monthly average PM2.5 for Palwal in 2017 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Palwal') & (data['Timestamp'].dt.year == 2017)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Palwal 2017', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Palwal') & (data['Timestamp'].dt.year == 2017)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Palwal 2017', width=450, height=280)


</code>
"
3547,temporal_aggregation,Show a monthly bar chart of the number of days Maharashtra exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2017.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Maharashtra') & (data['Timestamp'].dt.year == 2017)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Maharashtra Exceeded WHO PM2.5 Guideline per Month – 2017', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Maharashtra') & (data['Timestamp'].dt.year == 2017)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Maharashtra Exceeded WHO PM2.5 Guideline per Month – 2017', width=500, height=300)
     return chart


</code>
"
3548,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Meghalaya, Mizoram, and Meghalaya from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Meghalaya', 'Mizoram', 'Meghalaya']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Meghalaya vs Mizoram vs Meghalaya', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Meghalaya', 'Mizoram', 'Meghalaya']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Meghalaya vs Mizoram vs Meghalaya', width=550, height=320)
     return chart


</code>
"
3549,temporal_aggregation,Show a monthly bar chart of the number of days Sikkim exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2023.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Sikkim') & (data['Timestamp'].dt.year == 2023)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Sikkim Exceeded WHO PM2.5 Guideline per Month – 2023', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Sikkim') & (data['Timestamp'].dt.year == 2023)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Sikkim Exceeded WHO PM2.5 Guideline per Month – 2023', width=500, height=300)
     return chart


</code>
"
3550,specific_pattern,Show a cumulative area chart of PM2.5 readings for Mahad across 2023.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Mahad') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Mahad 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Mahad') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Mahad 2023', width=600, height=300)
     return chart


</code>
"
3551,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Damoh, Mangalore, and Dharuhera in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Damoh', 'Mangalore', 'Dharuhera']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Damoh vs Mangalore vs Dharuhera – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Damoh', 'Mangalore', 'Dharuhera']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Damoh vs Mangalore vs Dharuhera – 2024', width=550, height=320)
     return chart


</code>
"
3552,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Karwar, Baran, and Dhanbad in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Karwar', 'Baran', 'Dhanbad']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Karwar vs Baran vs Dhanbad – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Karwar', 'Baran', 'Dhanbad']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Karwar vs Baran vs Dhanbad – 2023', width=550, height=320)
     return chart


</code>
"
3553,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Sikkim, Uttarakhand, and Nagaland in 2021 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Sikkim', 'Uttarakhand', 'Nagaland']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Sikkim, Uttarakhand, UP – 2021', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Sikkim', 'Uttarakhand', 'Nagaland']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Sikkim, Uttarakhand, UP – 2021', width=550, height=320)
     return chart


</code>
"
3554,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Tripura, Tamil Nadu, and Madhya Pradesh from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Tripura', 'Tamil Nadu', 'Madhya Pradesh']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Tripura vs Tamil Nadu vs Madhya Pradesh', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Tripura', 'Tamil Nadu', 'Madhya Pradesh']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Tripura vs Tamil Nadu vs Madhya Pradesh', width=550, height=320)
     return chart


</code>
"
3555,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Rajasthan, Sikkim, and Mizoram across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Rajasthan', 'Sikkim', 'Mizoram']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Rajasthan', 'Sikkim', 'Mizoram']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
3556,specific_pattern,Plot the rolling 30-day average PM2.5 for Sikkim in 2020 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Sikkim') & (data['Timestamp'].dt.year == 2020)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Sikkim 2020', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Sikkim') & (data['Timestamp'].dt.year == 2020)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Sikkim 2020', width=600, height=300)


</code>
"
3557,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Virudhunagar, Yamuna Nagar, and Angul in 2019 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Virudhunagar', 'Yamuna Nagar', 'Angul']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Virudhunagar vs Yamuna Nagar vs Angul – 2019', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Virudhunagar', 'Yamuna Nagar', 'Angul']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Virudhunagar vs Yamuna Nagar vs Angul – 2019', width=550, height=320)
     return chart


</code>
"
3558,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Chhattisgarh, Jharkhand, and Haryana across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Chhattisgarh', 'Jharkhand', 'Haryana']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Chhattisgarh', 'Jharkhand', 'Haryana']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
3559,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Madhya Pradesh, Chandigarh, and Rajasthan in 2019 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Madhya Pradesh', 'Chandigarh', 'Rajasthan']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Madhya Pradesh, Chandigarh, UP – 2019', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Madhya Pradesh', 'Chandigarh', 'Rajasthan']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Madhya Pradesh, Chandigarh, UP – 2019', width=550, height=320)
     return chart


</code>
"
3560,specific_pattern,Show a cumulative area chart of PM2.5 readings for Belapur across 2024.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Belapur') & (data['Timestamp'].dt.year == 2024)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Belapur 2024', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Belapur') & (data['Timestamp'].dt.year == 2024)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Belapur 2024', width=600, height=300)
     return chart


</code>
"
3561,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Telangana, Assam, and Chhattisgarh from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Telangana', 'Assam', 'Chhattisgarh']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Telangana vs Assam vs Chhattisgarh', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Telangana', 'Assam', 'Chhattisgarh']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Telangana vs Assam vs Chhattisgarh', width=550, height=320)
     return chart


</code>
"
3562,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Mizoram, Telangana, and Kerala in 2019 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Mizoram', 'Telangana', 'Kerala']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Mizoram, Telangana, UP – 2019', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Mizoram', 'Telangana', 'Kerala']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Mizoram, Telangana, UP – 2019', width=550, height=320)
     return chart


</code>
"
3563,specific_pattern,Show a cumulative area chart of PM2.5 readings for Kanpur across 2018.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Kanpur') & (data['Timestamp'].dt.year == 2018)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Kanpur 2018', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Kanpur') & (data['Timestamp'].dt.year == 2018)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Kanpur 2018', width=600, height=300)
     return chart


</code>
"
3564,specific_pattern,Show a cumulative area chart of PM2.5 readings for Tumakuru across 2024.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Tumakuru') & (data['Timestamp'].dt.year == 2024)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Tumakuru 2024', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Tumakuru') & (data['Timestamp'].dt.year == 2024)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Tumakuru 2024', width=600, height=300)
     return chart


</code>
"
3565,temporal_aggregation,Show the monthly average PM10 trend for Maihar from 2017 to 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Maihar'].copy()
    df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Maihar (2017–2022)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Maihar'].copy()
     df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Maihar (2017–2022)', width=600, height=300)
     return chart


</code>
"
3566,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Bharatpur, Eloor, and Satna in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Bharatpur', 'Eloor', 'Satna']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Bharatpur vs Eloor vs Satna – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Bharatpur', 'Eloor', 'Satna']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Bharatpur vs Eloor vs Satna – 2023', width=550, height=320)
     return chart


</code>
"
3567,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Ernakulam, Rourkela, and Bhagalpur in 2020 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Ernakulam', 'Rourkela', 'Bhagalpur']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2020)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Ernakulam vs Rourkela vs Bhagalpur – 2020', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Ernakulam', 'Rourkela', 'Bhagalpur']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2020)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Ernakulam vs Rourkela vs Bhagalpur – 2020', width=550, height=320)
     return chart


</code>
"
3568,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Tripura, Telangana, and West Bengal across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Tripura', 'Telangana', 'West Bengal']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Tripura', 'Telangana', 'West Bengal']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
3569,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Mizoram, Manipur, and Meghalaya across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Mizoram', 'Manipur', 'Meghalaya']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Mizoram', 'Manipur', 'Meghalaya']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
3570,temporal_aggregation,Show the monthly average PM10 trend for Patna from 2017 to 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Patna'].copy()
    df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Patna (2017–2022)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Patna'].copy()
     df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Patna (2017–2022)', width=600, height=300)
     return chart


</code>
"
3571,specific_pattern,Show a cumulative area chart of PM2.5 readings for Guwahati across 2022.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Guwahati') & (data['Timestamp'].dt.year == 2022)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Guwahati 2022', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Guwahati') & (data['Timestamp'].dt.year == 2022)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Guwahati 2022', width=600, height=300)
     return chart


</code>
"
3572,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Angul, Thiruvananthapuram, and Jalore in 2020 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Angul', 'Thiruvananthapuram', 'Jalore']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2020)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Angul vs Thiruvananthapuram vs Jalore – 2020', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Angul', 'Thiruvananthapuram', 'Jalore']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2020)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Angul vs Thiruvananthapuram vs Jalore – 2020', width=550, height=320)
     return chart


</code>
"
3573,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Kerala, Uttarakhand, and Bihar across 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Kerala', 'Uttarakhand', 'Bihar']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Kerala', 'Uttarakhand', 'Bihar']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
     return chart


</code>
"
3574,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Hyderabad, Kozhikode, and Araria in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Hyderabad', 'Kozhikode', 'Araria']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Hyderabad vs Kozhikode vs Araria – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Hyderabad', 'Kozhikode', 'Araria']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Hyderabad vs Kozhikode vs Araria – 2022', width=550, height=320)
     return chart


</code>
"
3575,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Delhi, Arunachal Pradesh, and Telangana in 2018 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Delhi', 'Arunachal Pradesh', 'Telangana']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2018)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Delhi, Arunachal Pradesh, UP – 2018', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Delhi', 'Arunachal Pradesh', 'Telangana']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2018)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Delhi, Arunachal Pradesh, UP – 2018', width=550, height=320)
     return chart


</code>
"
3576,temporal_aggregation,Show the monthly average PM10 trend for Gwalior from 2017 to 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Gwalior'].copy()
    df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Gwalior (2017–2022)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Gwalior'].copy()
     df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Gwalior (2017–2022)', width=600, height=300)
     return chart


</code>
"
3577,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Punjab, Himachal Pradesh, and Telangana across 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Punjab', 'Himachal Pradesh', 'Telangana']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Punjab', 'Himachal Pradesh', 'Telangana']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
     return chart


</code>
"
3578,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Manipur, Himachal Pradesh, and Kerala across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Manipur', 'Himachal Pradesh', 'Kerala']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Manipur', 'Himachal Pradesh', 'Kerala']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
3579,temporal_aggregation,Show the monthly average PM10 trend for Sikar from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Sikar'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Sikar (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Sikar'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Sikar (2019–2024)', width=600, height=300)
     return chart


</code>
"
3580,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Chhattisgarh, Chandigarh, and Chandigarh across 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Chhattisgarh', 'Chandigarh', 'Chandigarh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Chhattisgarh', 'Chandigarh', 'Chandigarh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
     return chart


</code>
"
3581,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Manipur, Andhra Pradesh, and Uttarakhand in 2018 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Manipur', 'Andhra Pradesh', 'Uttarakhand']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2018)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Manipur, Andhra Pradesh, UP – 2018', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Manipur', 'Andhra Pradesh', 'Uttarakhand']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2018)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Manipur, Andhra Pradesh, UP – 2018', width=550, height=320)
     return chart


</code>
"
3582,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Telangana, Haryana, and Gujarat from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Telangana', 'Haryana', 'Gujarat']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Telangana vs Haryana vs Gujarat', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Telangana', 'Haryana', 'Gujarat']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Telangana vs Haryana vs Gujarat', width=550, height=320)
     return chart


</code>
"
3583,temporal_aggregation,Show the monthly average PM2.5 for Chennai in 2023 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Chennai') & (data['Timestamp'].dt.year == 2023)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Chennai 2023', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Chennai') & (data['Timestamp'].dt.year == 2023)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Chennai 2023', width=450, height=280)


</code>
"
3584,temporal_aggregation,Show the monthly average PM10 trend for Vellore from 2017 to 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Vellore'].copy()
    df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Vellore (2017–2022)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Vellore'].copy()
     df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Vellore (2017–2022)', width=600, height=300)
     return chart


</code>
"
3585,specific_pattern,Show a cumulative area chart of PM2.5 readings for Hisar across 2023.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Hisar') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Hisar 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Hisar') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Hisar 2023', width=600, height=300)
     return chart


</code>
"
3586,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Howrah, Sonipat, and Pune in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Howrah', 'Sonipat', 'Pune']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Howrah vs Sonipat vs Pune – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Howrah', 'Sonipat', 'Pune']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Howrah vs Sonipat vs Pune – 2023', width=550, height=320)
     return chart


</code>
"
3587,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Gujarat, Nagaland, and Puducherry across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Gujarat', 'Nagaland', 'Puducherry']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Gujarat', 'Nagaland', 'Puducherry']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
3588,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Pithampur, Dehradun, and Karnal in 2019 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Pithampur', 'Dehradun', 'Karnal']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Pithampur vs Dehradun vs Karnal – 2019', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Pithampur', 'Dehradun', 'Karnal']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Pithampur vs Dehradun vs Karnal – 2019', width=550, height=320)
     return chart


</code>
"
3589,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Andhra Pradesh, Manipur, and Punjab in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Andhra Pradesh', 'Manipur', 'Punjab']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Andhra Pradesh, Manipur, UP – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Andhra Pradesh', 'Manipur', 'Punjab']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Andhra Pradesh, Manipur, UP – 2022', width=550, height=320)
     return chart


</code>
"
3590,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Jammu and Kashmir, Meghalaya, and Uttar Pradesh across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Jammu and Kashmir', 'Meghalaya', 'Uttar Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Jammu and Kashmir', 'Meghalaya', 'Uttar Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
3591,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Karnataka, Odisha, and Chandigarh in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Karnataka', 'Odisha', 'Chandigarh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Karnataka, Odisha, UP – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Karnataka', 'Odisha', 'Chandigarh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Karnataka, Odisha, UP – 2023', width=550, height=320)
     return chart


</code>
"
3592,temporal_aggregation,Show the monthly average PM10 trend for Churu from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Churu'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Churu (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Churu'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Churu (2019–2024)', width=600, height=300)
     return chart


</code>
"
3593,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Himachal Pradesh, Telangana, and Odisha across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Himachal Pradesh', 'Telangana', 'Odisha']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Himachal Pradesh', 'Telangana', 'Odisha']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
3594,temporal_aggregation,Show a monthly bar chart of the number of days Maharashtra exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2020.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Maharashtra') & (data['Timestamp'].dt.year == 2020)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Maharashtra Exceeded WHO PM2.5 Guideline per Month – 2020', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Maharashtra') & (data['Timestamp'].dt.year == 2020)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Maharashtra Exceeded WHO PM2.5 Guideline per Month – 2020', width=500, height=300)
     return chart


</code>
"
3595,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Himachal Pradesh, Jammu and Kashmir, and Kerala in 2018 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Himachal Pradesh', 'Jammu and Kashmir', 'Kerala']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2018)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Himachal Pradesh, Jammu and Kashmir, UP – 2018', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Himachal Pradesh', 'Jammu and Kashmir', 'Kerala']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2018)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Himachal Pradesh, Jammu and Kashmir, UP – 2018', width=550, height=320)
     return chart


</code>
"
3596,temporal_aggregation,Show the monthly average PM2.5 for Purnia in 2018 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Purnia') & (data['Timestamp'].dt.year == 2018)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Purnia 2018', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Purnia') & (data['Timestamp'].dt.year == 2018)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Purnia 2018', width=450, height=280)


</code>
"
3597,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Assam, Maharashtra, and Chhattisgarh across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Assam', 'Maharashtra', 'Chhattisgarh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Assam', 'Maharashtra', 'Chhattisgarh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
3598,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Chandigarh, Telangana, and Bihar in 2019 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Chandigarh', 'Telangana', 'Bihar']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Chandigarh, Telangana, UP – 2019', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Chandigarh', 'Telangana', 'Bihar']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Chandigarh, Telangana, UP – 2019', width=550, height=320)
     return chart


</code>
"
3599,specific_pattern,Show a cumulative area chart of PM2.5 readings for Gummidipoondi across 2023.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Gummidipoondi') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Gummidipoondi 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Gummidipoondi') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Gummidipoondi 2023', width=600, height=300)
     return chart


</code>
"
3600,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Arunachal Pradesh, Himachal Pradesh, and Assam from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Arunachal Pradesh', 'Himachal Pradesh', 'Assam']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Arunachal Pradesh vs Himachal Pradesh vs Assam', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Arunachal Pradesh', 'Himachal Pradesh', 'Assam']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Arunachal Pradesh vs Himachal Pradesh vs Assam', width=550, height=320)
     return chart


</code>
"
3601,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Sikkim, Uttar Pradesh, and Kerala from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Sikkim', 'Uttar Pradesh', 'Kerala']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Sikkim vs Uttar Pradesh vs Kerala', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Sikkim', 'Uttar Pradesh', 'Kerala']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Sikkim vs Uttar Pradesh vs Kerala', width=550, height=320)
     return chart


</code>
"
3602,temporal_aggregation,Plot the weekly average PM2.5 for Vatva in 2023 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Vatva') & (data['Timestamp'].dt.year == 2023)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Vatva 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Vatva') & (data['Timestamp'].dt.year == 2023)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Vatva 2023', width=600, height=300)
     return chart


</code>
"
3603,temporal_aggregation,Plot the weekly average PM2.5 for Dehradun in 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Dehradun') & (data['Timestamp'].dt.year == 2024)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Dehradun 2024', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Dehradun') & (data['Timestamp'].dt.year == 2024)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Dehradun 2024', width=600, height=300)
     return chart


</code>
"
3604,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for West Bengal, Assam, and Assam across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['West Bengal', 'Assam', 'Assam']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['West Bengal', 'Assam', 'Assam']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
3605,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Manesar, Nayagarh, and Sawai Madhopur in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Manesar', 'Nayagarh', 'Sawai Madhopur']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Manesar vs Nayagarh vs Sawai Madhopur – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Manesar', 'Nayagarh', 'Sawai Madhopur']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Manesar vs Nayagarh vs Sawai Madhopur – 2022', width=550, height=320)
     return chart


</code>
"
3606,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Assam, Karnataka, and Telangana from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Assam', 'Karnataka', 'Telangana']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Assam vs Karnataka vs Telangana', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Assam', 'Karnataka', 'Telangana']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Assam vs Karnataka vs Telangana', width=550, height=320)
     return chart


</code>
"
3607,temporal_aggregation,Plot the weekly average PM2.5 for Noida in 2017 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Noida') & (data['Timestamp'].dt.year == 2017)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Noida 2017', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Noida') & (data['Timestamp'].dt.year == 2017)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Noida 2017', width=600, height=300)
     return chart


</code>
"
3608,specific_pattern,Show a cumulative area chart of PM2.5 readings for Rajamahendravaram across 2024.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Rajamahendravaram') & (data['Timestamp'].dt.year == 2024)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Rajamahendravaram 2024', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Rajamahendravaram') & (data['Timestamp'].dt.year == 2024)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Rajamahendravaram 2024', width=600, height=300)
     return chart


</code>
"
3609,specific_pattern,Show a cumulative area chart of PM2.5 readings for Tirupati across 2022.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Tirupati') & (data['Timestamp'].dt.year == 2022)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Tirupati 2022', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Tirupati') & (data['Timestamp'].dt.year == 2022)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Tirupati 2022', width=600, height=300)
     return chart


</code>
"
3610,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Meghalaya, Maharashtra, and Uttarakhand in 2021 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Meghalaya', 'Maharashtra', 'Uttarakhand']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Meghalaya, Maharashtra, UP – 2021', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Meghalaya', 'Maharashtra', 'Uttarakhand']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Meghalaya, Maharashtra, UP – 2021', width=550, height=320)
     return chart


</code>
"
3611,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Arunachal Pradesh, Andhra Pradesh, and Haryana in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Arunachal Pradesh', 'Andhra Pradesh', 'Haryana']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Arunachal Pradesh, Andhra Pradesh, UP – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Arunachal Pradesh', 'Andhra Pradesh', 'Haryana']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Arunachal Pradesh, Andhra Pradesh, UP – 2022', width=550, height=320)
     return chart


</code>
"
3612,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Korba, Thoothukudi, and Muzaffarnagar in 2020 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Korba', 'Thoothukudi', 'Muzaffarnagar']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2020)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Korba vs Thoothukudi vs Muzaffarnagar – 2020', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Korba', 'Thoothukudi', 'Muzaffarnagar']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2020)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Korba vs Thoothukudi vs Muzaffarnagar – 2020', width=550, height=320)
     return chart


</code>
"
3613,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for West Bengal, Haryana, and Punjab across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['West Bengal', 'Haryana', 'Punjab']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['West Bengal', 'Haryana', 'Punjab']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
3614,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Assam, Assam, and Sikkim from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Assam', 'Assam', 'Sikkim']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Assam vs Assam vs Sikkim', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Assam', 'Assam', 'Sikkim']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Assam vs Assam vs Sikkim', width=550, height=320)
     return chart


</code>
"
3615,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Bihar, Jammu and Kashmir, and Madhya Pradesh from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Bihar', 'Jammu and Kashmir', 'Madhya Pradesh']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Bihar vs Jammu and Kashmir vs Madhya Pradesh', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Bihar', 'Jammu and Kashmir', 'Madhya Pradesh']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Bihar vs Jammu and Kashmir vs Madhya Pradesh', width=550, height=320)
     return chart


</code>
"
3616,temporal_aggregation,Show a monthly bar chart of the number of days Madhya Pradesh exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2022.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Madhya Pradesh') & (data['Timestamp'].dt.year == 2022)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Madhya Pradesh Exceeded WHO PM2.5 Guideline per Month – 2022', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Madhya Pradesh') & (data['Timestamp'].dt.year == 2022)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Madhya Pradesh Exceeded WHO PM2.5 Guideline per Month – 2022', width=500, height=300)
     return chart


</code>
"
3617,temporal_aggregation,Plot the weekly average PM2.5 for Nalbari in 2023 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Nalbari') & (data['Timestamp'].dt.year == 2023)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Nalbari 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Nalbari') & (data['Timestamp'].dt.year == 2023)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Nalbari 2023', width=600, height=300)
     return chart


</code>
"
3618,temporal_aggregation,Show the monthly average PM2.5 for Bengaluru in 2020 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Bengaluru') & (data['Timestamp'].dt.year == 2020)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Bengaluru 2020', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Bengaluru') & (data['Timestamp'].dt.year == 2020)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Bengaluru 2020', width=450, height=280)


</code>
"
3619,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Jalna, Sri Ganganagar, and Pali in 2018 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Jalna', 'Sri Ganganagar', 'Pali']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2018)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Jalna vs Sri Ganganagar vs Pali – 2018', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Jalna', 'Sri Ganganagar', 'Pali']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2018)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Jalna vs Sri Ganganagar vs Pali – 2018', width=550, height=320)
     return chart


</code>
"
3620,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Ratlam, Sonipat, and Hyderabad in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Ratlam', 'Sonipat', 'Hyderabad']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Ratlam vs Sonipat vs Hyderabad – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Ratlam', 'Sonipat', 'Hyderabad']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Ratlam vs Sonipat vs Hyderabad – 2024', width=550, height=320)
     return chart


</code>
"
3621,specific_pattern,Plot the rolling 30-day average PM2.5 for Tamil Nadu in 2020 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Tamil Nadu') & (data['Timestamp'].dt.year == 2020)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Tamil Nadu 2020', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Tamil Nadu') & (data['Timestamp'].dt.year == 2020)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Tamil Nadu 2020', width=600, height=300)


</code>
"
3622,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Andhra Pradesh, Tripura, and Gujarat across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Andhra Pradesh', 'Tripura', 'Gujarat']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Andhra Pradesh', 'Tripura', 'Gujarat']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
3623,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for West Bengal, Karnataka, and Manipur from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['West Bengal', 'Karnataka', 'Manipur']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: West Bengal vs Karnataka vs Manipur', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['West Bengal', 'Karnataka', 'Manipur']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: West Bengal vs Karnataka vs Manipur', width=550, height=320)
     return chart


</code>
"
3624,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Meghalaya, Puducherry, and Jharkhand from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Meghalaya', 'Puducherry', 'Jharkhand']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Meghalaya vs Puducherry vs Jharkhand', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Meghalaya', 'Puducherry', 'Jharkhand']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Meghalaya vs Puducherry vs Jharkhand', width=550, height=320)
     return chart


</code>
"
3625,temporal_aggregation,Show the monthly average PM2.5 for Araria in 2019 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Araria') & (data['Timestamp'].dt.year == 2019)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Araria 2019', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Araria') & (data['Timestamp'].dt.year == 2019)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Araria 2019', width=450, height=280)


</code>
"
3626,specific_pattern,Plot the rolling 30-day average PM2.5 for Arunachal Pradesh in 2019 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Arunachal Pradesh') & (data['Timestamp'].dt.year == 2019)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Arunachal Pradesh 2019', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Arunachal Pradesh') & (data['Timestamp'].dt.year == 2019)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Arunachal Pradesh 2019', width=600, height=300)


</code>
"
3627,temporal_aggregation,Show the monthly average PM2.5 for Jodhpur in 2020 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Jodhpur') & (data['Timestamp'].dt.year == 2020)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Jodhpur 2020', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Jodhpur') & (data['Timestamp'].dt.year == 2020)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Jodhpur 2020', width=450, height=280)


</code>
"
3628,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Satna, Firozabad, and Kannur in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Satna', 'Firozabad', 'Kannur']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Satna vs Firozabad vs Kannur – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Satna', 'Firozabad', 'Kannur']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Satna vs Firozabad vs Kannur – 2022', width=550, height=320)
     return chart


</code>
"
3629,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Madikeri, Pudukottai, and Nashik in 2019 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Madikeri', 'Pudukottai', 'Nashik']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Madikeri vs Pudukottai vs Nashik – 2019', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Madikeri', 'Pudukottai', 'Nashik']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Madikeri vs Pudukottai vs Nashik – 2019', width=550, height=320)
     return chart


</code>
"
3630,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Chhattisgarh, Chhattisgarh, and Jammu and Kashmir across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Chhattisgarh', 'Chhattisgarh', 'Jammu and Kashmir']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Chhattisgarh', 'Chhattisgarh', 'Jammu and Kashmir']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
3631,temporal_aggregation,Plot the weekly average PM2.5 for Kolkata in 2019 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Kolkata') & (data['Timestamp'].dt.year == 2019)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Kolkata 2019', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Kolkata') & (data['Timestamp'].dt.year == 2019)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Kolkata 2019', width=600, height=300)
     return chart


</code>
"
3632,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Jammu and Kashmir, Bihar, and Puducherry from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Jammu and Kashmir', 'Bihar', 'Puducherry']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Jammu and Kashmir vs Bihar vs Puducherry', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Jammu and Kashmir', 'Bihar', 'Puducherry']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Jammu and Kashmir vs Bihar vs Puducherry', width=550, height=320)
     return chart


</code>
"
3633,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Haryana, Tripura, and Sikkim in 2017 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Haryana', 'Tripura', 'Sikkim']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2017)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Haryana, Tripura, UP – 2017', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Haryana', 'Tripura', 'Sikkim']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2017)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Haryana, Tripura, UP – 2017', width=550, height=320)
     return chart


</code>
"
3634,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Jharkhand, Jharkhand, and Sikkim in 2019 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Jharkhand', 'Jharkhand', 'Sikkim']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Jharkhand, Jharkhand, UP – 2019', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Jharkhand', 'Jharkhand', 'Sikkim']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Jharkhand, Jharkhand, UP – 2019', width=550, height=320)
     return chart


</code>
"
3635,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Mizoram, Sikkim, and Madhya Pradesh in 2017 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Mizoram', 'Sikkim', 'Madhya Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2017)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Mizoram, Sikkim, UP – 2017', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Mizoram', 'Sikkim', 'Madhya Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2017)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Mizoram, Sikkim, UP – 2017', width=550, height=320)
     return chart


</code>
"
3636,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Puducherry, Rajasthan, and Maharashtra in 2021 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Puducherry', 'Rajasthan', 'Maharashtra']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Puducherry, Rajasthan, UP – 2021', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Puducherry', 'Rajasthan', 'Maharashtra']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Puducherry, Rajasthan, UP – 2021', width=550, height=320)
     return chart


</code>
"
3637,temporal_aggregation,Show the monthly average PM10 trend for Agartala from 2017 to 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Agartala'].copy()
    df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Agartala (2017–2022)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Agartala'].copy()
     df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Agartala (2017–2022)', width=600, height=300)
     return chart


</code>
"
3638,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Sikkim, Manipur, and Arunachal Pradesh in 2021 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Sikkim', 'Manipur', 'Arunachal Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Sikkim, Manipur, UP – 2021', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Sikkim', 'Manipur', 'Arunachal Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Sikkim, Manipur, UP – 2021', width=550, height=320)
     return chart


</code>
"
3639,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Punjab stations in 2023, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Punjab') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Punjab Stations 2023', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Punjab') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Punjab Stations 2023', width=450, height=350)


</code>
"
3640,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Chhattisgarh, Maharashtra, and Arunachal Pradesh across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Chhattisgarh', 'Maharashtra', 'Arunachal Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Chhattisgarh', 'Maharashtra', 'Arunachal Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
3641,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Haryana, Manipur, and Nagaland in 2019 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Haryana', 'Manipur', 'Nagaland']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Haryana, Manipur, UP – 2019', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Haryana', 'Manipur', 'Nagaland']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Haryana, Manipur, UP – 2019', width=550, height=320)
     return chart


</code>
"
3642,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Rajasthan, Meghalaya, and Himachal Pradesh across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Rajasthan', 'Meghalaya', 'Himachal Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Rajasthan', 'Meghalaya', 'Himachal Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
3643,temporal_aggregation,Show the monthly average PM2.5 for Mahad in 2019 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Mahad') & (data['Timestamp'].dt.year == 2019)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Mahad 2019', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Mahad') & (data['Timestamp'].dt.year == 2019)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Mahad 2019', width=450, height=280)


</code>
"
3644,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Nagaland, Gujarat, and Nagaland across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Nagaland', 'Gujarat', 'Nagaland']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Nagaland', 'Gujarat', 'Nagaland']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
3645,temporal_aggregation,Show the monthly average PM10 trend for Jhunjhunu from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Jhunjhunu'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Jhunjhunu (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Jhunjhunu'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Jhunjhunu (2019–2024)', width=600, height=300)
     return chart


</code>
"
3646,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Uttarakhand, Nagaland, and Punjab in 2017 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Uttarakhand', 'Nagaland', 'Punjab']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2017)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Uttarakhand, Nagaland, UP – 2017', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Uttarakhand', 'Nagaland', 'Punjab']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2017)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Uttarakhand, Nagaland, UP – 2017', width=550, height=320)
     return chart


</code>
"
3647,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Jammu and Kashmir, Kerala, and Assam from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Jammu and Kashmir', 'Kerala', 'Assam']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Jammu and Kashmir vs Kerala vs Assam', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Jammu and Kashmir', 'Kerala', 'Assam']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Jammu and Kashmir vs Kerala vs Assam', width=550, height=320)
     return chart


</code>
"
3648,temporal_aggregation,Show the monthly average PM2.5 for Sivasagar in 2018 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Sivasagar') & (data['Timestamp'].dt.year == 2018)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Sivasagar 2018', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Sivasagar') & (data['Timestamp'].dt.year == 2018)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Sivasagar 2018', width=450, height=280)


</code>
"
3649,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Delhi, Chandigarh, and Haryana across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Delhi', 'Chandigarh', 'Haryana']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Delhi', 'Chandigarh', 'Haryana']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
3650,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Chhattisgarh, Chhattisgarh, and Puducherry from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Chhattisgarh', 'Chhattisgarh', 'Puducherry']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Chhattisgarh vs Chhattisgarh vs Puducherry', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Chhattisgarh', 'Chhattisgarh', 'Puducherry']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Chhattisgarh vs Chhattisgarh vs Puducherry', width=550, height=320)
     return chart


</code>
"
3651,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Tamil Nadu, Punjab, and Gujarat from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Tamil Nadu', 'Punjab', 'Gujarat']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Tamil Nadu vs Punjab vs Gujarat', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Tamil Nadu', 'Punjab', 'Gujarat']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Tamil Nadu vs Punjab vs Gujarat', width=550, height=320)
     return chart


</code>
"
3652,temporal_aggregation,Plot the weekly average PM2.5 for Ramanagara in 2023 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Ramanagara') & (data['Timestamp'].dt.year == 2023)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Ramanagara 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Ramanagara') & (data['Timestamp'].dt.year == 2023)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Ramanagara 2023', width=600, height=300)
     return chart


</code>
"
3653,temporal_aggregation,Show the monthly average PM2.5 for Gaya in 2018 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Gaya') & (data['Timestamp'].dt.year == 2018)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Gaya 2018', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Gaya') & (data['Timestamp'].dt.year == 2018)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Gaya 2018', width=450, height=280)


</code>
"
3654,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Madhya Pradesh, Mizoram, and Bihar from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Madhya Pradesh', 'Mizoram', 'Bihar']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Madhya Pradesh vs Mizoram vs Bihar', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Madhya Pradesh', 'Mizoram', 'Bihar']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Madhya Pradesh vs Mizoram vs Bihar', width=550, height=320)
     return chart


</code>
"
3655,specific_pattern,Show a cumulative area chart of PM2.5 readings for Haveri across 2022.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Haveri') & (data['Timestamp'].dt.year == 2022)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Haveri 2022', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Haveri') & (data['Timestamp'].dt.year == 2022)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Haveri 2022', width=600, height=300)
     return chart


</code>
"
3656,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Andhra Pradesh, Delhi, and Jammu and Kashmir across 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Andhra Pradesh', 'Delhi', 'Jammu and Kashmir']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Andhra Pradesh', 'Delhi', 'Jammu and Kashmir']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
     return chart


</code>
"
3657,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Virar, Tirupati, and Chhal in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Virar', 'Tirupati', 'Chhal']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Virar vs Tirupati vs Chhal – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Virar', 'Tirupati', 'Chhal']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Virar vs Tirupati vs Chhal – 2023', width=550, height=320)
     return chart


</code>
"
3658,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Nagaland, Uttarakhand, and Madhya Pradesh from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Nagaland', 'Uttarakhand', 'Madhya Pradesh']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Nagaland vs Uttarakhand vs Madhya Pradesh', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Nagaland', 'Uttarakhand', 'Madhya Pradesh']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Nagaland vs Uttarakhand vs Madhya Pradesh', width=550, height=320)
     return chart


</code>
"
3659,specific_pattern,Plot the rolling 30-day average PM2.5 for Manipur in 2023 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Manipur') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Manipur 2023', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Manipur') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Manipur 2023', width=600, height=300)


</code>
"
3660,temporal_aggregation,Plot the weekly average PM2.5 for Hajipur in 2023 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Hajipur') & (data['Timestamp'].dt.year == 2023)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Hajipur 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Hajipur') & (data['Timestamp'].dt.year == 2023)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Hajipur 2023', width=600, height=300)
     return chart


</code>
"
3661,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Odisha, Sikkim, and Puducherry across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Odisha', 'Sikkim', 'Puducherry']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Odisha', 'Sikkim', 'Puducherry']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
3662,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Nagaland, Jammu and Kashmir, and Haryana from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Nagaland', 'Jammu and Kashmir', 'Haryana']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Nagaland vs Jammu and Kashmir vs Haryana', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Nagaland', 'Jammu and Kashmir', 'Haryana']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Nagaland vs Jammu and Kashmir vs Haryana', width=550, height=320)
     return chart


</code>
"
3663,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Andhra Pradesh, Bihar, and Tamil Nadu across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Andhra Pradesh', 'Bihar', 'Tamil Nadu']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Andhra Pradesh', 'Bihar', 'Tamil Nadu']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
3664,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Himachal Pradesh stations in 2019, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Himachal Pradesh') & (data['Timestamp'].dt.year == 2019)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Himachal Pradesh Stations 2019', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Himachal Pradesh') & (data['Timestamp'].dt.year == 2019)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Himachal Pradesh Stations 2019', width=450, height=350)


</code>
"
3665,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Kerala, Haryana, and West Bengal in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Kerala', 'Haryana', 'West Bengal']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Kerala, Haryana, UP – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Kerala', 'Haryana', 'West Bengal']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Kerala, Haryana, UP – 2023', width=550, height=320)
     return chart


</code>
"
3666,temporal_aggregation,Show the monthly average PM2.5 for Ulhasnagar in 2020 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Ulhasnagar') & (data['Timestamp'].dt.year == 2020)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Ulhasnagar 2020', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Ulhasnagar') & (data['Timestamp'].dt.year == 2020)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Ulhasnagar 2020', width=450, height=280)


</code>
"
3667,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Jharkhand, Haryana, and Arunachal Pradesh in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Jharkhand', 'Haryana', 'Arunachal Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Jharkhand, Haryana, UP – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Jharkhand', 'Haryana', 'Arunachal Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Jharkhand, Haryana, UP – 2024', width=550, height=320)
     return chart


</code>
"
3668,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Himachal Pradesh, Tripura, and Sikkim from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Himachal Pradesh', 'Tripura', 'Sikkim']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Himachal Pradesh vs Tripura vs Sikkim', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Himachal Pradesh', 'Tripura', 'Sikkim']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Himachal Pradesh vs Tripura vs Sikkim', width=550, height=320)
     return chart


</code>
"
3669,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Jharkhand, Manipur, and Haryana from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Jharkhand', 'Manipur', 'Haryana']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Jharkhand vs Manipur vs Haryana', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Jharkhand', 'Manipur', 'Haryana']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Jharkhand vs Manipur vs Haryana', width=550, height=320)
     return chart


</code>
"
3670,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Assam, Madhya Pradesh, and Andhra Pradesh in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Assam', 'Madhya Pradesh', 'Andhra Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Assam, Madhya Pradesh, UP – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Assam', 'Madhya Pradesh', 'Andhra Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Assam, Madhya Pradesh, UP – 2022', width=550, height=320)
     return chart


</code>
"
3671,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Uttar Pradesh, Mizoram, and Himachal Pradesh across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Uttar Pradesh', 'Mizoram', 'Himachal Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Uttar Pradesh', 'Mizoram', 'Himachal Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
3672,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Pune, Rupnagar, and Karauli in 2020 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Pune', 'Rupnagar', 'Karauli']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2020)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Pune vs Rupnagar vs Karauli – 2020', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Pune', 'Rupnagar', 'Karauli']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2020)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Pune vs Rupnagar vs Karauli – 2020', width=550, height=320)
     return chart


</code>
"
3673,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Telangana, Mizoram, and Puducherry in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Telangana', 'Mizoram', 'Puducherry']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Telangana, Mizoram, UP – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Telangana', 'Mizoram', 'Puducherry']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Telangana, Mizoram, UP – 2023', width=550, height=320)
     return chart


</code>
"
3674,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Delhi, Chandigarh, and Jharkhand from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Delhi', 'Chandigarh', 'Jharkhand']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Delhi vs Chandigarh vs Jharkhand', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Delhi', 'Chandigarh', 'Jharkhand']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Delhi vs Chandigarh vs Jharkhand', width=550, height=320)
     return chart


</code>
"
3675,temporal_aggregation,Plot the weekly average PM2.5 for Dewas in 2023 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Dewas') & (data['Timestamp'].dt.year == 2023)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Dewas 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Dewas') & (data['Timestamp'].dt.year == 2023)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Dewas 2023', width=600, height=300)
     return chart


</code>
"
3676,temporal_aggregation,Plot the weekly average PM2.5 for Chittoor in 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Chittoor') & (data['Timestamp'].dt.year == 2024)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Chittoor 2024', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Chittoor') & (data['Timestamp'].dt.year == 2024)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Chittoor 2024', width=600, height=300)
     return chart


</code>
"
3677,specific_pattern,Plot the rolling 30-day average PM2.5 for Uttarakhand in 2017 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Uttarakhand') & (data['Timestamp'].dt.year == 2017)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Uttarakhand 2017', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Uttarakhand') & (data['Timestamp'].dt.year == 2017)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Uttarakhand 2017', width=600, height=300)


</code>
"
3678,temporal_aggregation,Show the monthly average PM10 trend for Haveri from 2017 to 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Haveri'].copy()
    df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Haveri (2017–2022)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Haveri'].copy()
     df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Haveri (2017–2022)', width=600, height=300)
     return chart


</code>
"
3679,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Bihar, Himachal Pradesh, and Tamil Nadu from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Bihar', 'Himachal Pradesh', 'Tamil Nadu']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Bihar vs Himachal Pradesh vs Tamil Nadu', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Bihar', 'Himachal Pradesh', 'Tamil Nadu']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Bihar vs Himachal Pradesh vs Tamil Nadu', width=550, height=320)
     return chart


</code>
"
3680,specific_pattern,Show a cumulative area chart of PM2.5 readings for Bileipada across 2022.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Bileipada') & (data['Timestamp'].dt.year == 2022)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Bileipada 2022', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Bileipada') & (data['Timestamp'].dt.year == 2022)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Bileipada 2022', width=600, height=300)
     return chart


</code>
"
3681,temporal_aggregation,Plot the weekly average PM2.5 for Pratapgarh in 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Pratapgarh') & (data['Timestamp'].dt.year == 2024)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Pratapgarh 2024', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Pratapgarh') & (data['Timestamp'].dt.year == 2024)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Pratapgarh 2024', width=600, height=300)
     return chart


</code>
"
3682,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Ulhasnagar, Badlapur, and Panipat in 2019 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Ulhasnagar', 'Badlapur', 'Panipat']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Ulhasnagar vs Badlapur vs Panipat – 2019', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Ulhasnagar', 'Badlapur', 'Panipat']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Ulhasnagar vs Badlapur vs Panipat – 2019', width=550, height=320)
     return chart


</code>
"
3683,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Mizoram, Kerala, and Jammu and Kashmir in 2019 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Mizoram', 'Kerala', 'Jammu and Kashmir']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Mizoram, Kerala, UP – 2019', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Mizoram', 'Kerala', 'Jammu and Kashmir']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Mizoram, Kerala, UP – 2019', width=550, height=320)
     return chart


</code>
"
3684,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Araria, Milupara, and Yamuna Nagar in 2019 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Araria', 'Milupara', 'Yamuna Nagar']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Araria vs Milupara vs Yamuna Nagar – 2019', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Araria', 'Milupara', 'Yamuna Nagar']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Araria vs Milupara vs Yamuna Nagar – 2019', width=550, height=320)
     return chart


</code>
"
3685,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Puducherry, Odisha, and Nagaland across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Puducherry', 'Odisha', 'Nagaland']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Puducherry', 'Odisha', 'Nagaland']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
3686,temporal_aggregation,Show the monthly average PM2.5 for Tumidih in 2023 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Tumidih') & (data['Timestamp'].dt.year == 2023)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Tumidih 2023', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Tumidih') & (data['Timestamp'].dt.year == 2023)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Tumidih 2023', width=450, height=280)


</code>
"
3687,temporal_aggregation,Show the monthly average PM10 trend for Chennai from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Chennai'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Chennai (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Chennai'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Chennai (2019–2024)', width=600, height=300)
     return chart


</code>
"
3688,temporal_aggregation,Show the monthly average PM2.5 for Damoh in 2019 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Damoh') & (data['Timestamp'].dt.year == 2019)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Damoh 2019', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Damoh') & (data['Timestamp'].dt.year == 2019)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Damoh 2019', width=450, height=280)


</code>
"
3689,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Jammu and Kashmir, Sikkim, and Delhi from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Jammu and Kashmir', 'Sikkim', 'Delhi']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Jammu and Kashmir vs Sikkim vs Delhi', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Jammu and Kashmir', 'Sikkim', 'Delhi']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Jammu and Kashmir vs Sikkim vs Delhi', width=550, height=320)
     return chart


</code>
"
3690,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Rajasthan, Chandigarh, and Punjab in 2018 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Rajasthan', 'Chandigarh', 'Punjab']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2018)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Rajasthan, Chandigarh, UP – 2018', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Rajasthan', 'Chandigarh', 'Punjab']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2018)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Rajasthan, Chandigarh, UP – 2018', width=550, height=320)
     return chart


</code>
"
3691,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Karauli, Brajrajnagar, and Hosur in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Karauli', 'Brajrajnagar', 'Hosur']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Karauli vs Brajrajnagar vs Hosur – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Karauli', 'Brajrajnagar', 'Hosur']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Karauli vs Brajrajnagar vs Hosur – 2023', width=550, height=320)
     return chart


</code>
"
3692,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Sikar, Salem, and Gaya in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Sikar', 'Salem', 'Gaya']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Sikar vs Salem vs Gaya – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Sikar', 'Salem', 'Gaya']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Sikar vs Salem vs Gaya – 2023', width=550, height=320)
     return chart


</code>
"
3693,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Gujarat, Delhi, and Assam across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Gujarat', 'Delhi', 'Assam']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Gujarat', 'Delhi', 'Assam']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
3694,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Puducherry, Jharkhand, and Jammu and Kashmir across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Puducherry', 'Jharkhand', 'Jammu and Kashmir']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Puducherry', 'Jharkhand', 'Jammu and Kashmir']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
3695,temporal_aggregation,Show the monthly average PM2.5 for Sivasagar in 2023 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Sivasagar') & (data['Timestamp'].dt.year == 2023)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Sivasagar 2023', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Sivasagar') & (data['Timestamp'].dt.year == 2023)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Sivasagar 2023', width=450, height=280)


</code>
"
3696,temporal_aggregation,Show the monthly average PM10 trend for Greater Noida from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Greater Noida'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Greater Noida (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Greater Noida'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Greater Noida (2019–2024)', width=600, height=300)
     return chart


</code>
"
3697,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Jharkhand, Jammu and Kashmir, and Gujarat in 2019 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Jharkhand', 'Jammu and Kashmir', 'Gujarat']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Jharkhand, Jammu and Kashmir, UP – 2019', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Jharkhand', 'Jammu and Kashmir', 'Gujarat']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Jharkhand, Jammu and Kashmir, UP – 2019', width=550, height=320)
     return chart


</code>
"
3698,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Alwar, Chandigarh, and Agra in 2018 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Alwar', 'Chandigarh', 'Agra']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2018)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Alwar vs Chandigarh vs Agra – 2018', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Alwar', 'Chandigarh', 'Agra']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2018)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Alwar vs Chandigarh vs Agra – 2018', width=550, height=320)
     return chart


</code>
"
3699,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Gujarat, Uttar Pradesh, and Telangana from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Gujarat', 'Uttar Pradesh', 'Telangana']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Gujarat vs Uttar Pradesh vs Telangana', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Gujarat', 'Uttar Pradesh', 'Telangana']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Gujarat vs Uttar Pradesh vs Telangana', width=550, height=320)
     return chart


</code>
"
3700,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Himachal Pradesh, Maharashtra, and Nagaland from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Himachal Pradesh', 'Maharashtra', 'Nagaland']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Himachal Pradesh vs Maharashtra vs Nagaland', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Himachal Pradesh', 'Maharashtra', 'Nagaland']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Himachal Pradesh vs Maharashtra vs Nagaland', width=550, height=320)
     return chart


</code>
"
3701,temporal_aggregation,Show the monthly average PM10 trend for Muzaffarpur from 2017 to 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Muzaffarpur'].copy()
    df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Muzaffarpur (2017–2022)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Muzaffarpur'].copy()
     df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Muzaffarpur (2017–2022)', width=600, height=300)
     return chart


</code>
"
3702,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Hassan, Raipur, and Rohtak in 2018 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Hassan', 'Raipur', 'Rohtak']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2018)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Hassan vs Raipur vs Rohtak – 2018', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Hassan', 'Raipur', 'Rohtak']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2018)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Hassan vs Raipur vs Rohtak – 2018', width=550, height=320)
     return chart


</code>
"
3703,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Alwar, Rairangpur, and Baddi in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Alwar', 'Rairangpur', 'Baddi']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Alwar vs Rairangpur vs Baddi – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Alwar', 'Rairangpur', 'Baddi']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Alwar vs Rairangpur vs Baddi – 2022', width=550, height=320)
     return chart


</code>
"
3704,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Karnataka, Tripura, and Kerala from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Karnataka', 'Tripura', 'Kerala']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Karnataka vs Tripura vs Kerala', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Karnataka', 'Tripura', 'Kerala']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Karnataka vs Tripura vs Kerala', width=550, height=320)
     return chart


</code>
"
3705,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Puducherry, Chhapra, and Rajamahendravaram in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Puducherry', 'Chhapra', 'Rajamahendravaram']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Puducherry vs Chhapra vs Rajamahendravaram – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Puducherry', 'Chhapra', 'Rajamahendravaram']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Puducherry vs Chhapra vs Rajamahendravaram – 2024', width=550, height=320)
     return chart


</code>
"
3706,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Ghaziabad, Gurugram, and Hanumangarh in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Ghaziabad', 'Gurugram', 'Hanumangarh']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Ghaziabad vs Gurugram vs Hanumangarh – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Ghaziabad', 'Gurugram', 'Hanumangarh']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Ghaziabad vs Gurugram vs Hanumangarh – 2023', width=550, height=320)
     return chart


</code>
"
3707,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Puducherry, Arunachal Pradesh, and Sikkim across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Puducherry', 'Arunachal Pradesh', 'Sikkim']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Puducherry', 'Arunachal Pradesh', 'Sikkim']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
3708,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Bihar, Delhi, and Chhattisgarh from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Bihar', 'Delhi', 'Chhattisgarh']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Bihar vs Delhi vs Chhattisgarh', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Bihar', 'Delhi', 'Chhattisgarh']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Bihar vs Delhi vs Chhattisgarh', width=550, height=320)
     return chart


</code>
"
3709,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Mandideep, Gurugram, and Kaithal in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Mandideep', 'Gurugram', 'Kaithal']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Mandideep vs Gurugram vs Kaithal – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Mandideep', 'Gurugram', 'Kaithal']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Mandideep vs Gurugram vs Kaithal – 2023', width=550, height=320)
     return chart


</code>
"
3710,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Puducherry, Meghalaya, and Assam in 2021 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Puducherry', 'Meghalaya', 'Assam']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Puducherry, Meghalaya, UP – 2021', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Puducherry', 'Meghalaya', 'Assam']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Puducherry, Meghalaya, UP – 2021', width=550, height=320)
     return chart


</code>
"
3711,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Bhilai, Bhubaneswar, and Tirupur in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Bhilai', 'Bhubaneswar', 'Tirupur']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Bhilai vs Bhubaneswar vs Tirupur – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Bhilai', 'Bhubaneswar', 'Tirupur']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Bhilai vs Bhubaneswar vs Tirupur – 2023', width=550, height=320)
     return chart


</code>
"
3712,specific_pattern,Show a cumulative area chart of PM2.5 readings for Mandi Gobindgarh across 2021.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Mandi Gobindgarh') & (data['Timestamp'].dt.year == 2021)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Mandi Gobindgarh 2021', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Mandi Gobindgarh') & (data['Timestamp'].dt.year == 2021)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Mandi Gobindgarh 2021', width=600, height=300)
     return chart


</code>
"
3713,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Sikkim, Mizoram, and Delhi in 2021 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Sikkim', 'Mizoram', 'Delhi']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Sikkim, Mizoram, UP – 2021', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Sikkim', 'Mizoram', 'Delhi']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Sikkim, Mizoram, UP – 2021', width=550, height=320)
     return chart


</code>
"
3714,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Delhi, Sikkim, and Telangana across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Delhi', 'Sikkim', 'Telangana']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Delhi', 'Sikkim', 'Telangana']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
3715,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Hassan, Mangalore, and Belgaum in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Hassan', 'Mangalore', 'Belgaum']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Hassan vs Mangalore vs Belgaum – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Hassan', 'Mangalore', 'Belgaum']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Hassan vs Mangalore vs Belgaum – 2022', width=550, height=320)
     return chart


</code>
"
3716,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Tripura, Delhi, and Chandigarh across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Tripura', 'Delhi', 'Chandigarh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Tripura', 'Delhi', 'Chandigarh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
3717,temporal_aggregation,Show the monthly average PM2.5 for Akola in 2022 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Akola') & (data['Timestamp'].dt.year == 2022)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Akola 2022', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Akola') & (data['Timestamp'].dt.year == 2022)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Akola 2022', width=450, height=280)


</code>
"
3718,temporal_aggregation,Show the monthly average PM2.5 for Jhalawar in 2020 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Jhalawar') & (data['Timestamp'].dt.year == 2020)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Jhalawar 2020', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Jhalawar') & (data['Timestamp'].dt.year == 2020)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Jhalawar 2020', width=450, height=280)


</code>
"
3719,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Agra, Bhiwadi, and Akola in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Agra', 'Bhiwadi', 'Akola']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Agra vs Bhiwadi vs Akola – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Agra', 'Bhiwadi', 'Akola']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Agra vs Bhiwadi vs Akola – 2022', width=550, height=320)
     return chart


</code>
"
3720,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Korba, Ambala, and Rupnagar in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Korba', 'Ambala', 'Rupnagar']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Korba vs Ambala vs Rupnagar – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Korba', 'Ambala', 'Rupnagar']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Korba vs Ambala vs Rupnagar – 2024', width=550, height=320)
     return chart


</code>
"
3721,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Manipur, Uttarakhand, and Puducherry from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Manipur', 'Uttarakhand', 'Puducherry']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Manipur vs Uttarakhand vs Puducherry', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Manipur', 'Uttarakhand', 'Puducherry']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Manipur vs Uttarakhand vs Puducherry', width=550, height=320)
     return chart


</code>
"
3722,temporal_aggregation,Show the monthly average PM2.5 for Eloor in 2020 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Eloor') & (data['Timestamp'].dt.year == 2020)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Eloor 2020', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Eloor') & (data['Timestamp'].dt.year == 2020)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Eloor 2020', width=450, height=280)


</code>
"
3723,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Gujarat, Chhattisgarh, and Puducherry across 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Gujarat', 'Chhattisgarh', 'Puducherry']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Gujarat', 'Chhattisgarh', 'Puducherry']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
     return chart


</code>
"
3724,specific_pattern,Show a cumulative area chart of PM2.5 readings for Vellore across 2022.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Vellore') & (data['Timestamp'].dt.year == 2022)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Vellore 2022', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Vellore') & (data['Timestamp'].dt.year == 2022)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Vellore 2022', width=600, height=300)
     return chart


</code>
"
3725,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Assam stations in 2024, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Assam') & (data['Timestamp'].dt.year == 2024)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Assam Stations 2024', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Assam') & (data['Timestamp'].dt.year == 2024)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Assam Stations 2024', width=450, height=350)


</code>
"
3726,temporal_aggregation,Plot the weekly average PM2.5 for Gangtok in 2023 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Gangtok') & (data['Timestamp'].dt.year == 2023)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Gangtok 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Gangtok') & (data['Timestamp'].dt.year == 2023)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Gangtok 2023', width=600, height=300)
     return chart


</code>
"
3727,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Nagaland, Tamil Nadu, and Karnataka across 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Nagaland', 'Tamil Nadu', 'Karnataka']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Nagaland', 'Tamil Nadu', 'Karnataka']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
     return chart


</code>
"
3728,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Rajsamand, Thoothukudi, and Maihar in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Rajsamand', 'Thoothukudi', 'Maihar']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Rajsamand vs Thoothukudi vs Maihar – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Rajsamand', 'Thoothukudi', 'Maihar']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Rajsamand vs Thoothukudi vs Maihar – 2023', width=550, height=320)
     return chart


</code>
"
3729,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Madhya Pradesh, Tripura, and Uttarakhand in 2019 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Madhya Pradesh', 'Tripura', 'Uttarakhand']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Madhya Pradesh, Tripura, UP – 2019', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Madhya Pradesh', 'Tripura', 'Uttarakhand']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Madhya Pradesh, Tripura, UP – 2019', width=550, height=320)
     return chart


</code>
"
3730,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Chennai, Asansol, and Kurukshetra in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Chennai', 'Asansol', 'Kurukshetra']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Chennai vs Asansol vs Kurukshetra – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Chennai', 'Asansol', 'Kurukshetra']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Chennai vs Asansol vs Kurukshetra – 2024', width=550, height=320)
     return chart


</code>
"
3731,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Odisha, Haryana, and Nagaland from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Odisha', 'Haryana', 'Nagaland']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Odisha vs Haryana vs Nagaland', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Odisha', 'Haryana', 'Nagaland']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Odisha vs Haryana vs Nagaland', width=550, height=320)
     return chart


</code>
"
3732,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Telangana, Tamil Nadu, and Jharkhand in 2021 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Telangana', 'Tamil Nadu', 'Jharkhand']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Telangana, Tamil Nadu, UP – 2021', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Telangana', 'Tamil Nadu', 'Jharkhand']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Telangana, Tamil Nadu, UP – 2021', width=550, height=320)
     return chart


</code>
"
3733,temporal_aggregation,Show the monthly average PM2.5 for Chamarajanagar in 2018 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Chamarajanagar') & (data['Timestamp'].dt.year == 2018)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Chamarajanagar 2018', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Chamarajanagar') & (data['Timestamp'].dt.year == 2018)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Chamarajanagar 2018', width=450, height=280)


</code>
"
3734,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Karnataka, Nagaland, and Himachal Pradesh in 2021 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Karnataka', 'Nagaland', 'Himachal Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Karnataka, Nagaland, UP – 2021', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Karnataka', 'Nagaland', 'Himachal Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Karnataka, Nagaland, UP – 2021', width=550, height=320)
     return chart


</code>
"
3735,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Tripura, Madhya Pradesh, and Meghalaya from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Tripura', 'Madhya Pradesh', 'Meghalaya']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Tripura vs Madhya Pradesh vs Meghalaya', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Tripura', 'Madhya Pradesh', 'Meghalaya']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Tripura vs Madhya Pradesh vs Meghalaya', width=550, height=320)
     return chart


</code>
"
3736,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Delhi, Himachal Pradesh, and Uttar Pradesh across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Delhi', 'Himachal Pradesh', 'Uttar Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Delhi', 'Himachal Pradesh', 'Uttar Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
3737,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Punjab, West Bengal, and Tamil Nadu across 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Punjab', 'West Bengal', 'Tamil Nadu']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Punjab', 'West Bengal', 'Tamil Nadu']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
     return chart


</code>
"
3738,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Gujarat, Tripura, and Delhi across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Gujarat', 'Tripura', 'Delhi']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Gujarat', 'Tripura', 'Delhi']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
3739,temporal_aggregation,Show the monthly average PM10 trend for Gaya from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Gaya'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Gaya (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Gaya'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Gaya (2019–2024)', width=600, height=300)
     return chart


</code>
"
3740,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Andhra Pradesh, Telangana, and Kerala in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Andhra Pradesh', 'Telangana', 'Kerala']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Andhra Pradesh, Telangana, UP – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Andhra Pradesh', 'Telangana', 'Kerala']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Andhra Pradesh, Telangana, UP – 2024', width=550, height=320)
     return chart


</code>
"
3741,temporal_aggregation,Plot the weekly average PM2.5 for Mandideep in 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Mandideep') & (data['Timestamp'].dt.year == 2024)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Mandideep 2024', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Mandideep') & (data['Timestamp'].dt.year == 2024)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Mandideep 2024', width=600, height=300)
     return chart


</code>
"
3742,specific_pattern,Show a cumulative area chart of PM2.5 readings for Chamarajanagar across 2022.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Chamarajanagar') & (data['Timestamp'].dt.year == 2022)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Chamarajanagar 2022', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Chamarajanagar') & (data['Timestamp'].dt.year == 2022)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Chamarajanagar 2022', width=600, height=300)
     return chart


</code>
"
3743,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Uttarakhand, Karnataka, and Bihar in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Uttarakhand', 'Karnataka', 'Bihar']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Uttarakhand, Karnataka, UP – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Uttarakhand', 'Karnataka', 'Bihar']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Uttarakhand, Karnataka, UP – 2023', width=550, height=320)
     return chart


</code>
"
3744,temporal_aggregation,Plot the weekly average PM2.5 for Bulandshahr in 2021 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Bulandshahr') & (data['Timestamp'].dt.year == 2021)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Bulandshahr 2021', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Bulandshahr') & (data['Timestamp'].dt.year == 2021)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Bulandshahr 2021', width=600, height=300)
     return chart


</code>
"
3745,temporal_aggregation,Show the monthly average PM10 trend for Sirsa from 2017 to 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Sirsa'].copy()
    df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Sirsa (2017–2022)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Sirsa'].copy()
     df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Sirsa (2017–2022)', width=600, height=300)
     return chart


</code>
"
3746,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Uttarakhand, Kerala, and Manipur in 2021 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Uttarakhand', 'Kerala', 'Manipur']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Uttarakhand, Kerala, UP – 2021', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Uttarakhand', 'Kerala', 'Manipur']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Uttarakhand, Kerala, UP – 2021', width=550, height=320)
     return chart


</code>
"
3747,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Arunachal Pradesh, West Bengal, and West Bengal across 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Arunachal Pradesh', 'West Bengal', 'West Bengal']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Arunachal Pradesh', 'West Bengal', 'West Bengal']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
     return chart


</code>
"
3748,temporal_aggregation,Plot the weekly average PM2.5 for Udupi in 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Udupi') & (data['Timestamp'].dt.year == 2024)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Udupi 2024', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Udupi') & (data['Timestamp'].dt.year == 2024)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Udupi 2024', width=600, height=300)
     return chart


</code>
"
3749,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Puducherry, Punjab, and Himachal Pradesh across 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Puducherry', 'Punjab', 'Himachal Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Puducherry', 'Punjab', 'Himachal Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
     return chart


</code>
"
3750,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Faridabad, Shivamogga, and Chennai in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Faridabad', 'Shivamogga', 'Chennai']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Faridabad vs Shivamogga vs Chennai – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Faridabad', 'Shivamogga', 'Chennai']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Faridabad vs Shivamogga vs Chennai – 2022', width=550, height=320)
     return chart


</code>
"
3751,temporal_aggregation,Show the monthly average PM2.5 for Ratlam in 2017 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Ratlam') & (data['Timestamp'].dt.year == 2017)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Ratlam 2017', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Ratlam') & (data['Timestamp'].dt.year == 2017)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Ratlam 2017', width=450, height=280)


</code>
"
3752,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Bihar, Jharkhand, and Rajasthan across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Bihar', 'Jharkhand', 'Rajasthan']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Bihar', 'Jharkhand', 'Rajasthan']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
3753,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Andhra Pradesh, Nagaland, and West Bengal from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Andhra Pradesh', 'Nagaland', 'West Bengal']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Andhra Pradesh vs Nagaland vs West Bengal', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Andhra Pradesh', 'Nagaland', 'West Bengal']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Andhra Pradesh vs Nagaland vs West Bengal', width=550, height=320)
     return chart


</code>
"
3754,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Rajamahendravaram, Araria, and Jalgaon in 2017 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Rajamahendravaram', 'Araria', 'Jalgaon']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2017)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Rajamahendravaram vs Araria vs Jalgaon – 2017', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Rajamahendravaram', 'Araria', 'Jalgaon']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2017)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Rajamahendravaram vs Araria vs Jalgaon – 2017', width=550, height=320)
     return chart


</code>
"
3755,temporal_aggregation,Show the monthly average PM2.5 for Tumakuru in 2017 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Tumakuru') & (data['Timestamp'].dt.year == 2017)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Tumakuru 2017', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Tumakuru') & (data['Timestamp'].dt.year == 2017)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Tumakuru 2017', width=450, height=280)


</code>
"
3756,temporal_aggregation,Show the monthly average PM10 trend for Bihar Sharif from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Bihar Sharif'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Bihar Sharif (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Bihar Sharif'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Bihar Sharif (2019–2024)', width=600, height=300)
     return chart


</code>
"
3757,temporal_aggregation,Show the monthly average PM10 trend for Katni from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Katni'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Katni (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Katni'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Katni (2019–2024)', width=600, height=300)
     return chart


</code>
"
3758,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Maharashtra, Nagaland, and Arunachal Pradesh in 2021 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Maharashtra', 'Nagaland', 'Arunachal Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Maharashtra, Nagaland, UP – 2021', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Maharashtra', 'Nagaland', 'Arunachal Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Maharashtra, Nagaland, UP – 2021', width=550, height=320)
     return chart


</code>
"
3759,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Puducherry, Raipur, and Boisar in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Puducherry', 'Raipur', 'Boisar']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Puducherry vs Raipur vs Boisar – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Puducherry', 'Raipur', 'Boisar']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Puducherry vs Raipur vs Boisar – 2023', width=550, height=320)
     return chart


</code>
"
3760,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Tripura stations in 2023, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Tripura') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Tripura Stations 2023', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Tripura') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Tripura Stations 2023', width=450, height=350)


</code>
"
3761,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Haryana, Rajasthan, and Nagaland across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Haryana', 'Rajasthan', 'Nagaland']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Haryana', 'Rajasthan', 'Nagaland']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
3762,specific_pattern,Plot the rolling 30-day average PM2.5 for Karnataka in 2020 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Karnataka') & (data['Timestamp'].dt.year == 2020)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Karnataka 2020', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Karnataka') & (data['Timestamp'].dt.year == 2020)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Karnataka 2020', width=600, height=300)


</code>
"
3763,temporal_aggregation,Show the monthly average PM2.5 for Bilaspur in 2019 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Bilaspur') & (data['Timestamp'].dt.year == 2019)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Bilaspur 2019', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Bilaspur') & (data['Timestamp'].dt.year == 2019)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Bilaspur 2019', width=450, height=280)


</code>
"
3764,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Jharkhand, Himachal Pradesh, and Nagaland in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Jharkhand', 'Himachal Pradesh', 'Nagaland']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Jharkhand, Himachal Pradesh, UP – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Jharkhand', 'Himachal Pradesh', 'Nagaland']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Jharkhand, Himachal Pradesh, UP – 2024', width=550, height=320)
     return chart


</code>
"
3765,specific_pattern,Show a cumulative area chart of PM2.5 readings for Patna across 2024.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Patna') & (data['Timestamp'].dt.year == 2024)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Patna 2024', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Patna') & (data['Timestamp'].dt.year == 2024)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Patna 2024', width=600, height=300)
     return chart


</code>
"
3766,temporal_aggregation,Show the monthly average PM2.5 for Satna in 2017 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Satna') & (data['Timestamp'].dt.year == 2017)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Satna 2017', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Satna') & (data['Timestamp'].dt.year == 2017)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Satna 2017', width=450, height=280)


</code>
"
3767,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Karnataka, Uttar Pradesh, and Meghalaya from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Karnataka', 'Uttar Pradesh', 'Meghalaya']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Karnataka vs Uttar Pradesh vs Meghalaya', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Karnataka', 'Uttar Pradesh', 'Meghalaya']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Karnataka vs Uttar Pradesh vs Meghalaya', width=550, height=320)
     return chart


</code>
"
3768,specific_pattern,Plot the rolling 30-day average PM2.5 for Tripura in 2018 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Tripura') & (data['Timestamp'].dt.year == 2018)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Tripura 2018', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Tripura') & (data['Timestamp'].dt.year == 2018)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Tripura 2018', width=600, height=300)


</code>
"
3769,spatio_temporal_aggregation,"Visualize the monthly average PM10 for West Bengal, Delhi, and Mizoram in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['West Bengal', 'Delhi', 'Mizoram']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: West Bengal, Delhi, UP – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['West Bengal', 'Delhi', 'Mizoram']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: West Bengal, Delhi, UP – 2023', width=550, height=320)
     return chart


</code>
"
3770,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Arunachal Pradesh, Arunachal Pradesh, and Mizoram across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Arunachal Pradesh', 'Arunachal Pradesh', 'Mizoram']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Arunachal Pradesh', 'Arunachal Pradesh', 'Mizoram']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
3771,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Odisha, Meghalaya, and Madhya Pradesh in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Odisha', 'Meghalaya', 'Madhya Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Odisha, Meghalaya, UP – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Odisha', 'Meghalaya', 'Madhya Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Odisha, Meghalaya, UP – 2022', width=550, height=320)
     return chart


</code>
"
3772,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Gujarat, Uttar Pradesh, and Rajasthan across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Gujarat', 'Uttar Pradesh', 'Rajasthan']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Gujarat', 'Uttar Pradesh', 'Rajasthan']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
3773,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Gujarat, Nagaland, and Madhya Pradesh across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Gujarat', 'Nagaland', 'Madhya Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Gujarat', 'Nagaland', 'Madhya Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
3774,specific_pattern,Show a cumulative area chart of PM2.5 readings for Satna across 2022.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Satna') & (data['Timestamp'].dt.year == 2022)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Satna 2022', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Satna') & (data['Timestamp'].dt.year == 2022)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Satna 2022', width=600, height=300)
     return chart


</code>
"
3775,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Himachal Pradesh, Chandigarh, and Odisha across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Himachal Pradesh', 'Chandigarh', 'Odisha']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Himachal Pradesh', 'Chandigarh', 'Odisha']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
3776,temporal_aggregation,Show the monthly average PM2.5 for Karnal in 2022 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Karnal') & (data['Timestamp'].dt.year == 2022)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Karnal 2022', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Karnal') & (data['Timestamp'].dt.year == 2022)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Karnal 2022', width=450, height=280)


</code>
"
3777,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Odisha, Sikkim, and Uttar Pradesh in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Odisha', 'Sikkim', 'Uttar Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Odisha, Sikkim, UP – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Odisha', 'Sikkim', 'Uttar Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Odisha, Sikkim, UP – 2023', width=550, height=320)
     return chart


</code>
"
3778,temporal_aggregation,Show the monthly average PM10 trend for Nandesari from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Nandesari'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Nandesari (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Nandesari'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Nandesari (2019–2024)', width=600, height=300)
     return chart


</code>
"
3779,temporal_aggregation,Show the monthly average PM10 trend for Ballabgarh from 2017 to 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Ballabgarh'].copy()
    df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Ballabgarh (2017–2022)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Ballabgarh'].copy()
     df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Ballabgarh (2017–2022)', width=600, height=300)
     return chart


</code>
"
3780,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Madhya Pradesh, Gujarat, and Delhi from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Madhya Pradesh', 'Gujarat', 'Delhi']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Madhya Pradesh vs Gujarat vs Delhi', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Madhya Pradesh', 'Gujarat', 'Delhi']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Madhya Pradesh vs Gujarat vs Delhi', width=550, height=320)
     return chart


</code>
"
3781,specific_pattern,Plot the rolling 30-day average PM2.5 for Uttarakhand in 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Uttarakhand') & (data['Timestamp'].dt.year == 2024)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Uttarakhand 2024', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Uttarakhand') & (data['Timestamp'].dt.year == 2024)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Uttarakhand 2024', width=600, height=300)


</code>
"
3782,temporal_aggregation,Show the monthly average PM10 trend for Shivamogga from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Shivamogga'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Shivamogga (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Shivamogga'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Shivamogga (2019–2024)', width=600, height=300)
     return chart


</code>
"
3783,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Odisha, West Bengal, and Haryana across 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Odisha', 'West Bengal', 'Haryana']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Odisha', 'West Bengal', 'Haryana']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
     return chart


</code>
"
3784,temporal_aggregation,Plot the weekly average PM2.5 for Hisar in 2020 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Hisar') & (data['Timestamp'].dt.year == 2020)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Hisar 2020', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Hisar') & (data['Timestamp'].dt.year == 2020)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Hisar 2020', width=600, height=300)
     return chart


</code>
"
3785,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Jhalawar, Gandhinagar, and Mira-Bhayandar in 2020 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Jhalawar', 'Gandhinagar', 'Mira-Bhayandar']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2020)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Jhalawar vs Gandhinagar vs Mira-Bhayandar – 2020', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Jhalawar', 'Gandhinagar', 'Mira-Bhayandar']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2020)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Jhalawar vs Gandhinagar vs Mira-Bhayandar – 2020', width=550, height=320)
     return chart


</code>
"
3786,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Khanna, Baripada, and Badlapur in 2018 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Khanna', 'Baripada', 'Badlapur']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2018)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Khanna vs Baripada vs Badlapur – 2018', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Khanna', 'Baripada', 'Badlapur']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2018)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Khanna vs Baripada vs Badlapur – 2018', width=550, height=320)
     return chart


</code>
"
3787,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Odisha, Assam, and Mizoram in 2019 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Odisha', 'Assam', 'Mizoram']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Odisha, Assam, UP – 2019', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Odisha', 'Assam', 'Mizoram']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Odisha, Assam, UP – 2019', width=550, height=320)
     return chart


</code>
"
3788,temporal_aggregation,Show the monthly average PM10 trend for Tirupur from 2017 to 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Tirupur'].copy()
    df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Tirupur (2017–2022)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Tirupur'].copy()
     df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Tirupur (2017–2022)', width=600, height=300)
     return chart


</code>
"
3789,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for West Bengal, Tamil Nadu, and Bihar across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['West Bengal', 'Tamil Nadu', 'Bihar']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['West Bengal', 'Tamil Nadu', 'Bihar']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
3790,temporal_aggregation,Show the monthly average PM2.5 for Korba in 2017 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Korba') & (data['Timestamp'].dt.year == 2017)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Korba 2017', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Korba') & (data['Timestamp'].dt.year == 2017)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Korba 2017', width=450, height=280)


</code>
"
3791,temporal_aggregation,Show the monthly average PM2.5 for Rupnagar in 2024 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Rupnagar') & (data['Timestamp'].dt.year == 2024)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Rupnagar 2024', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Rupnagar') & (data['Timestamp'].dt.year == 2024)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Rupnagar 2024', width=450, height=280)


</code>
"
3792,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Chhattisgarh, Assam, and Kerala in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Chhattisgarh', 'Assam', 'Kerala']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Chhattisgarh, Assam, UP – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Chhattisgarh', 'Assam', 'Kerala']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Chhattisgarh, Assam, UP – 2024', width=550, height=320)
     return chart


</code>
"
3793,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Jharkhand, Tripura, and Karnataka across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Jharkhand', 'Tripura', 'Karnataka']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Jharkhand', 'Tripura', 'Karnataka']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
3794,temporal_aggregation,Show the monthly average PM2.5 for Bhiwadi in 2020 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Bhiwadi') & (data['Timestamp'].dt.year == 2020)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Bhiwadi 2020', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Bhiwadi') & (data['Timestamp'].dt.year == 2020)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Bhiwadi 2020', width=450, height=280)


</code>
"
3795,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Mandi Gobindgarh, Madurai, and Lucknow in 2020 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Mandi Gobindgarh', 'Madurai', 'Lucknow']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2020)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Mandi Gobindgarh vs Madurai vs Lucknow – 2020', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Mandi Gobindgarh', 'Madurai', 'Lucknow']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2020)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Mandi Gobindgarh vs Madurai vs Lucknow – 2020', width=550, height=320)
     return chart


</code>
"
3796,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Arunachal Pradesh, Mizoram, and Kerala across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Arunachal Pradesh', 'Mizoram', 'Kerala']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Arunachal Pradesh', 'Mizoram', 'Kerala']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
3797,specific_pattern,Show a cumulative area chart of PM2.5 readings for Greater Noida across 2022.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Greater Noida') & (data['Timestamp'].dt.year == 2022)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Greater Noida 2022', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Greater Noida') & (data['Timestamp'].dt.year == 2022)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Greater Noida 2022', width=600, height=300)
     return chart


</code>
"
3798,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Delhi, Tamil Nadu, and Mizoram in 2019 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Delhi', 'Tamil Nadu', 'Mizoram']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Delhi, Tamil Nadu, UP – 2019', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Delhi', 'Tamil Nadu', 'Mizoram']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Delhi, Tamil Nadu, UP – 2019', width=550, height=320)
     return chart


</code>
"
3799,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Sikkim, Uttarakhand, and Punjab from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Sikkim', 'Uttarakhand', 'Punjab']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Sikkim vs Uttarakhand vs Punjab', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Sikkim', 'Uttarakhand', 'Punjab']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Sikkim vs Uttarakhand vs Punjab', width=550, height=320)
     return chart


</code>
"
3800,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Andhra Pradesh, Tripura, and Odisha from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Andhra Pradesh', 'Tripura', 'Odisha']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Andhra Pradesh vs Tripura vs Odisha', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Andhra Pradesh', 'Tripura', 'Odisha']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Andhra Pradesh vs Tripura vs Odisha', width=550, height=320)
     return chart


</code>
"
3801,temporal_aggregation,Show the monthly average PM2.5 for Brajrajnagar in 2018 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Brajrajnagar') & (data['Timestamp'].dt.year == 2018)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Brajrajnagar 2018', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Brajrajnagar') & (data['Timestamp'].dt.year == 2018)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Brajrajnagar 2018', width=450, height=280)


</code>
"
3802,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Madurai, Maihar, and Tirupur in 2020 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Madurai', 'Maihar', 'Tirupur']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2020)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Madurai vs Maihar vs Tirupur – 2020', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Madurai', 'Maihar', 'Tirupur']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2020)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Madurai vs Maihar vs Tirupur – 2020', width=550, height=320)
     return chart


</code>
"
3803,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Delhi, Karnataka, and Himachal Pradesh from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Delhi', 'Karnataka', 'Himachal Pradesh']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Delhi vs Karnataka vs Himachal Pradesh', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Delhi', 'Karnataka', 'Himachal Pradesh']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Delhi vs Karnataka vs Himachal Pradesh', width=550, height=320)
     return chart


</code>
"
3804,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Delhi, Sikkim, and Delhi from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Delhi', 'Sikkim', 'Delhi']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Delhi vs Sikkim vs Delhi', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Delhi', 'Sikkim', 'Delhi']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Delhi vs Sikkim vs Delhi', width=550, height=320)
     return chart


</code>
"
3805,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Maharashtra, Jammu and Kashmir, and Punjab in 2017 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Maharashtra', 'Jammu and Kashmir', 'Punjab']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2017)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Maharashtra, Jammu and Kashmir, UP – 2017', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Maharashtra', 'Jammu and Kashmir', 'Punjab']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2017)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Maharashtra, Jammu and Kashmir, UP – 2017', width=550, height=320)
     return chart


</code>
"
3806,temporal_aggregation,Show the monthly average PM2.5 for Banswara in 2020 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Banswara') & (data['Timestamp'].dt.year == 2020)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Banswara 2020', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Banswara') & (data['Timestamp'].dt.year == 2020)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Banswara 2020', width=450, height=280)


</code>
"
3807,specific_pattern,Show a cumulative area chart of PM2.5 readings for Dharwad across 2024.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Dharwad') & (data['Timestamp'].dt.year == 2024)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Dharwad 2024', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Dharwad') & (data['Timestamp'].dt.year == 2024)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Dharwad 2024', width=600, height=300)
     return chart


</code>
"
3808,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Tripura, Uttarakhand, and Delhi from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Tripura', 'Uttarakhand', 'Delhi']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Tripura vs Uttarakhand vs Delhi', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Tripura', 'Uttarakhand', 'Delhi']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Tripura vs Uttarakhand vs Delhi', width=550, height=320)
     return chart


</code>
"
3809,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Mizoram, Sikkim, and Gujarat in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Mizoram', 'Sikkim', 'Gujarat']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Mizoram, Sikkim, UP – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Mizoram', 'Sikkim', 'Gujarat']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Mizoram, Sikkim, UP – 2023', width=550, height=320)
     return chart


</code>
"
3810,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Assam, Odisha, and Maharashtra in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Assam', 'Odisha', 'Maharashtra']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Assam, Odisha, UP – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Assam', 'Odisha', 'Maharashtra']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Assam, Odisha, UP – 2023', width=550, height=320)
     return chart


</code>
"
3811,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Narnaul, Bagalkot, and Kohima in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Narnaul', 'Bagalkot', 'Kohima']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Narnaul vs Bagalkot vs Kohima – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Narnaul', 'Bagalkot', 'Kohima']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Narnaul vs Bagalkot vs Kohima – 2023', width=550, height=320)
     return chart


</code>
"
3812,temporal_aggregation,Show the monthly average PM2.5 for Mysuru in 2024 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Mysuru') & (data['Timestamp'].dt.year == 2024)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Mysuru 2024', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Mysuru') & (data['Timestamp'].dt.year == 2024)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Mysuru 2024', width=450, height=280)


</code>
"
3813,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Sangli, Akola, and Eloor in 2020 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Sangli', 'Akola', 'Eloor']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2020)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Sangli vs Akola vs Eloor – 2020', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Sangli', 'Akola', 'Eloor']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2020)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Sangli vs Akola vs Eloor – 2020', width=550, height=320)
     return chart


</code>
"
3814,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Odisha, Rajasthan, and West Bengal across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Odisha', 'Rajasthan', 'West Bengal']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Odisha', 'Rajasthan', 'West Bengal']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
3815,temporal_aggregation,Show the monthly average PM10 trend for Barrackpore from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Barrackpore'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Barrackpore (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Barrackpore'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Barrackpore (2019–2024)', width=600, height=300)
     return chart


</code>
"
3816,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Rajasthan, Andhra Pradesh, and Bihar across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Rajasthan', 'Andhra Pradesh', 'Bihar']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Rajasthan', 'Andhra Pradesh', 'Bihar']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
3817,temporal_aggregation,Show the monthly average PM2.5 for Sonipat in 2018 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Sonipat') & (data['Timestamp'].dt.year == 2018)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Sonipat 2018', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Sonipat') & (data['Timestamp'].dt.year == 2018)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Sonipat 2018', width=450, height=280)


</code>
"
3818,specific_pattern,Show a cumulative area chart of PM2.5 readings for Sagar across 2022.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Sagar') & (data['Timestamp'].dt.year == 2022)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Sagar 2022', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Sagar') & (data['Timestamp'].dt.year == 2022)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Sagar 2022', width=600, height=300)
     return chart


</code>
"
3819,temporal_aggregation,Show the monthly average PM10 trend for Surat from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Surat'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Surat (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Surat'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Surat (2019–2024)', width=600, height=300)
     return chart


</code>
"
3820,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Himachal Pradesh, Andhra Pradesh, and Kerala across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Himachal Pradesh', 'Andhra Pradesh', 'Kerala']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Himachal Pradesh', 'Andhra Pradesh', 'Kerala']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
3821,temporal_aggregation,Show the monthly average PM2.5 for Kunjemura in 2023 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Kunjemura') & (data['Timestamp'].dt.year == 2023)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Kunjemura 2023', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Kunjemura') & (data['Timestamp'].dt.year == 2023)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Kunjemura 2023', width=450, height=280)


</code>
"
3822,temporal_aggregation,Show the monthly average PM2.5 for Ulhasnagar in 2019 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Ulhasnagar') & (data['Timestamp'].dt.year == 2019)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Ulhasnagar 2019', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Ulhasnagar') & (data['Timestamp'].dt.year == 2019)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Ulhasnagar 2019', width=450, height=280)


</code>
"
3823,temporal_aggregation,Plot the weekly average PM2.5 for Indore in 2021 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Indore') & (data['Timestamp'].dt.year == 2021)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Indore 2021', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Indore') & (data['Timestamp'].dt.year == 2021)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Indore 2021', width=600, height=300)
     return chart


</code>
"
3824,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Chandigarh, Tamil Nadu, and Karnataka across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Chandigarh', 'Tamil Nadu', 'Karnataka']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Chandigarh', 'Tamil Nadu', 'Karnataka']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
3825,temporal_aggregation,Show the monthly average PM2.5 for Nagaon in 2018 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Nagaon') & (data['Timestamp'].dt.year == 2018)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Nagaon 2018', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Nagaon') & (data['Timestamp'].dt.year == 2018)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Nagaon 2018', width=450, height=280)


</code>
"
3826,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Delhi, Arunachal Pradesh, and Odisha in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Delhi', 'Arunachal Pradesh', 'Odisha']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Delhi, Arunachal Pradesh, UP – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Delhi', 'Arunachal Pradesh', 'Odisha']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Delhi, Arunachal Pradesh, UP – 2023', width=550, height=320)
     return chart


</code>
"
3827,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Gujarat, Telangana, and Jammu and Kashmir from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Gujarat', 'Telangana', 'Jammu and Kashmir']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Gujarat vs Telangana vs Jammu and Kashmir', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Gujarat', 'Telangana', 'Jammu and Kashmir']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Gujarat vs Telangana vs Jammu and Kashmir', width=550, height=320)
     return chart


</code>
"
3828,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Arunachal Pradesh, Nagaland, and Chandigarh across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Arunachal Pradesh', 'Nagaland', 'Chandigarh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Arunachal Pradesh', 'Nagaland', 'Chandigarh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
3829,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Buxar, Chandrapur, and Suakati in 2020 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Buxar', 'Chandrapur', 'Suakati']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2020)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Buxar vs Chandrapur vs Suakati – 2020', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Buxar', 'Chandrapur', 'Suakati']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2020)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Buxar vs Chandrapur vs Suakati – 2020', width=550, height=320)
     return chart


</code>
"
3830,temporal_aggregation,Show the monthly average PM2.5 for Mysuru in 2019 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Mysuru') & (data['Timestamp'].dt.year == 2019)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Mysuru 2019', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Mysuru') & (data['Timestamp'].dt.year == 2019)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Mysuru 2019', width=450, height=280)


</code>
"
3831,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Mizoram, Tamil Nadu, and Chhattisgarh from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Mizoram', 'Tamil Nadu', 'Chhattisgarh']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Mizoram vs Tamil Nadu vs Chhattisgarh', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Mizoram', 'Tamil Nadu', 'Chhattisgarh']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Mizoram vs Tamil Nadu vs Chhattisgarh', width=550, height=320)
     return chart


</code>
"
3832,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Uttar Pradesh, Andhra Pradesh, and Uttarakhand across 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Uttar Pradesh', 'Andhra Pradesh', 'Uttarakhand']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Uttar Pradesh', 'Andhra Pradesh', 'Uttarakhand']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
     return chart


</code>
"
3833,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Haryana, Odisha, and Puducherry in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Haryana', 'Odisha', 'Puducherry']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Haryana, Odisha, UP – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Haryana', 'Odisha', 'Puducherry']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Haryana, Odisha, UP – 2022', width=550, height=320)
     return chart


</code>
"
3834,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Punjab, Uttarakhand, and Assam in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Punjab', 'Uttarakhand', 'Assam']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Punjab, Uttarakhand, UP – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Punjab', 'Uttarakhand', 'Assam']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Punjab, Uttarakhand, UP – 2022', width=550, height=320)
     return chart


</code>
"
3835,specific_pattern,Show a cumulative area chart of PM2.5 readings for Nagaur across 2024.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Nagaur') & (data['Timestamp'].dt.year == 2024)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Nagaur 2024', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Nagaur') & (data['Timestamp'].dt.year == 2024)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Nagaur 2024', width=600, height=300)
     return chart


</code>
"
3836,temporal_aggregation,Show the monthly average PM2.5 for Bikaner in 2018 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Bikaner') & (data['Timestamp'].dt.year == 2018)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Bikaner 2018', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Bikaner') & (data['Timestamp'].dt.year == 2018)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Bikaner 2018', width=450, height=280)


</code>
"
3837,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Karnataka stations in 2021, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Karnataka') & (data['Timestamp'].dt.year == 2021)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Karnataka Stations 2021', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Karnataka') & (data['Timestamp'].dt.year == 2021)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Karnataka Stations 2021', width=450, height=350)


</code>
"
3838,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Karnal, Manguraha, and Gandhinagar in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Karnal', 'Manguraha', 'Gandhinagar']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Karnal vs Manguraha vs Gandhinagar – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Karnal', 'Manguraha', 'Gandhinagar']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Karnal vs Manguraha vs Gandhinagar – 2022', width=550, height=320)
     return chart


</code>
"
3839,spatio_temporal_aggregation,"Visualize the monthly average PM10 for West Bengal, Madhya Pradesh, and Jammu and Kashmir in 2019 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['West Bengal', 'Madhya Pradesh', 'Jammu and Kashmir']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: West Bengal, Madhya Pradesh, UP – 2019', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['West Bengal', 'Madhya Pradesh', 'Jammu and Kashmir']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: West Bengal, Madhya Pradesh, UP – 2019', width=550, height=320)
     return chart


</code>
"
3840,temporal_aggregation,Plot the weekly average PM2.5 for Kollam in 2021 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Kollam') & (data['Timestamp'].dt.year == 2021)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Kollam 2021', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Kollam') & (data['Timestamp'].dt.year == 2021)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Kollam 2021', width=600, height=300)
     return chart


</code>
"
3841,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Jammu and Kashmir, Haryana, and Punjab from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Jammu and Kashmir', 'Haryana', 'Punjab']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Jammu and Kashmir vs Haryana vs Punjab', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Jammu and Kashmir', 'Haryana', 'Punjab']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Jammu and Kashmir vs Haryana vs Punjab', width=550, height=320)
     return chart


</code>
"
3842,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Haryana, Uttarakhand, and Karnataka from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Haryana', 'Uttarakhand', 'Karnataka']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Haryana vs Uttarakhand vs Karnataka', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Haryana', 'Uttarakhand', 'Karnataka']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Haryana vs Uttarakhand vs Karnataka', width=550, height=320)
     return chart


</code>
"
3843,temporal_aggregation,Show the monthly average PM2.5 for Chhapra in 2019 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Chhapra') & (data['Timestamp'].dt.year == 2019)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Chhapra 2019', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Chhapra') & (data['Timestamp'].dt.year == 2019)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Chhapra 2019', width=450, height=280)


</code>
"
3844,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Puducherry, Assam, and Telangana across 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Puducherry', 'Assam', 'Telangana']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Puducherry', 'Assam', 'Telangana']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
     return chart


</code>
"
3845,temporal_aggregation,Plot the weekly average PM2.5 for Gaya in 2023 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Gaya') & (data['Timestamp'].dt.year == 2023)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Gaya 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Gaya') & (data['Timestamp'].dt.year == 2023)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Gaya 2023', width=600, height=300)
     return chart


</code>
"
3846,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Jharkhand, Tamil Nadu, and Rajasthan across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Jharkhand', 'Tamil Nadu', 'Rajasthan']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Jharkhand', 'Tamil Nadu', 'Rajasthan']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
3847,specific_pattern,Show a cumulative area chart of PM2.5 readings for Pali across 2023.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Pali') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Pali 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Pali') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Pali 2023', width=600, height=300)
     return chart


</code>
"
3848,temporal_aggregation,Show the monthly average PM2.5 for Chamarajanagar in 2022 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Chamarajanagar') & (data['Timestamp'].dt.year == 2022)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Chamarajanagar 2022', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Chamarajanagar') & (data['Timestamp'].dt.year == 2022)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Chamarajanagar 2022', width=450, height=280)


</code>
"
3849,temporal_aggregation,Show a monthly bar chart of the number of days Jammu and Kashmir exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2021.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Jammu and Kashmir') & (data['Timestamp'].dt.year == 2021)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Jammu and Kashmir Exceeded WHO PM2.5 Guideline per Month – 2021', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Jammu and Kashmir') & (data['Timestamp'].dt.year == 2021)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Jammu and Kashmir Exceeded WHO PM2.5 Guideline per Month – 2021', width=500, height=300)
     return chart


</code>
"
3850,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Madhya Pradesh, Karnataka, and Uttar Pradesh in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Madhya Pradesh', 'Karnataka', 'Uttar Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Madhya Pradesh, Karnataka, UP – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Madhya Pradesh', 'Karnataka', 'Uttar Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Madhya Pradesh, Karnataka, UP – 2023', width=550, height=320)
     return chart


</code>
"
3851,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for West Bengal stations in 2019, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'West Bengal') & (data['Timestamp'].dt.year == 2019)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – West Bengal Stations 2019', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'West Bengal') & (data['Timestamp'].dt.year == 2019)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – West Bengal Stations 2019', width=450, height=350)


</code>
"
3852,temporal_aggregation,Show the monthly average PM2.5 for Thiruvananthapuram in 2017 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Thiruvananthapuram') & (data['Timestamp'].dt.year == 2017)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Thiruvananthapuram 2017', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Thiruvananthapuram') & (data['Timestamp'].dt.year == 2017)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Thiruvananthapuram 2017', width=450, height=280)


</code>
"
3853,temporal_aggregation,Show the monthly average PM10 trend for Bilaspur from 2017 to 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Bilaspur'].copy()
    df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Bilaspur (2017–2022)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Bilaspur'].copy()
     df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Bilaspur (2017–2022)', width=600, height=300)
     return chart


</code>
"
3854,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Meghalaya, Chhattisgarh, and Haryana in 2019 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Meghalaya', 'Chhattisgarh', 'Haryana']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Meghalaya, Chhattisgarh, UP – 2019', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Meghalaya', 'Chhattisgarh', 'Haryana']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Meghalaya, Chhattisgarh, UP – 2019', width=550, height=320)
     return chart


</code>
"
3855,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Shillong, Noida, and Dindigul in 2020 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Shillong', 'Noida', 'Dindigul']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2020)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Shillong vs Noida vs Dindigul – 2020', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Shillong', 'Noida', 'Dindigul']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2020)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Shillong vs Noida vs Dindigul – 2020', width=550, height=320)
     return chart


</code>
"
3856,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Andhra Pradesh, Chhattisgarh, and Chandigarh in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Andhra Pradesh', 'Chhattisgarh', 'Chandigarh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Andhra Pradesh, Chhattisgarh, UP – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Andhra Pradesh', 'Chhattisgarh', 'Chandigarh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Andhra Pradesh, Chhattisgarh, UP – 2024', width=550, height=320)
     return chart


</code>
"
3857,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Prayagraj, Udaipur, and Jaisalmer in 2018 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Prayagraj', 'Udaipur', 'Jaisalmer']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2018)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Prayagraj vs Udaipur vs Jaisalmer – 2018', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Prayagraj', 'Udaipur', 'Jaisalmer']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2018)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Prayagraj vs Udaipur vs Jaisalmer – 2018', width=550, height=320)
     return chart


</code>
"
3858,temporal_aggregation,Show the monthly average PM2.5 for Udaipur in 2023 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Udaipur') & (data['Timestamp'].dt.year == 2023)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Udaipur 2023', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Udaipur') & (data['Timestamp'].dt.year == 2023)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Udaipur 2023', width=450, height=280)


</code>
"
3859,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Uttarakhand, Himachal Pradesh, and Himachal Pradesh across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Uttarakhand', 'Himachal Pradesh', 'Himachal Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Uttarakhand', 'Himachal Pradesh', 'Himachal Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
3860,specific_pattern,Show a cumulative area chart of PM2.5 readings for Sonipat across 2024.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Sonipat') & (data['Timestamp'].dt.year == 2024)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Sonipat 2024', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Sonipat') & (data['Timestamp'].dt.year == 2024)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Sonipat 2024', width=600, height=300)
     return chart


</code>
"
3861,temporal_aggregation,Show the monthly average PM10 trend for Baddi from 2017 to 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Baddi'].copy()
    df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Baddi (2017–2022)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Baddi'].copy()
     df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Baddi (2017–2022)', width=600, height=300)
     return chart


</code>
"
3862,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Punjab, Delhi, and Arunachal Pradesh in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Punjab', 'Delhi', 'Arunachal Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Punjab, Delhi, UP – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Punjab', 'Delhi', 'Arunachal Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Punjab, Delhi, UP – 2024', width=550, height=320)
     return chart


</code>
"
3863,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Himachal Pradesh, Assam, and Uttar Pradesh across 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Himachal Pradesh', 'Assam', 'Uttar Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Himachal Pradesh', 'Assam', 'Uttar Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
     return chart


</code>
"
3864,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Mizoram, Maharashtra, and Chhattisgarh across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Mizoram', 'Maharashtra', 'Chhattisgarh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Mizoram', 'Maharashtra', 'Chhattisgarh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
3865,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Mizoram, Jammu and Kashmir, and Meghalaya across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Mizoram', 'Jammu and Kashmir', 'Meghalaya']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Mizoram', 'Jammu and Kashmir', 'Meghalaya']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
3866,temporal_aggregation,Show the monthly average PM2.5 for Gandhinagar in 2020 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Gandhinagar') & (data['Timestamp'].dt.year == 2020)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Gandhinagar 2020', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Gandhinagar') & (data['Timestamp'].dt.year == 2020)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Gandhinagar 2020', width=450, height=280)


</code>
"
3867,temporal_aggregation,Show a monthly bar chart of the number of days Telangana exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2024.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Telangana') & (data['Timestamp'].dt.year == 2024)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Telangana Exceeded WHO PM2.5 Guideline per Month – 2024', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Telangana') & (data['Timestamp'].dt.year == 2024)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Telangana Exceeded WHO PM2.5 Guideline per Month – 2024', width=500, height=300)
     return chart


</code>
"
3868,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Rajasthan, Rajasthan, and Uttar Pradesh across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Rajasthan', 'Rajasthan', 'Uttar Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Rajasthan', 'Rajasthan', 'Uttar Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
3869,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Jharkhand, Uttarakhand, and Uttar Pradesh from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Jharkhand', 'Uttarakhand', 'Uttar Pradesh']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Jharkhand vs Uttarakhand vs Uttar Pradesh', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Jharkhand', 'Uttarakhand', 'Uttar Pradesh']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Jharkhand vs Uttarakhand vs Uttar Pradesh', width=550, height=320)
     return chart


</code>
"
3870,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Assam, Nagaland, and Manipur from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Assam', 'Nagaland', 'Manipur']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Assam vs Nagaland vs Manipur', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Assam', 'Nagaland', 'Manipur']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Assam vs Nagaland vs Manipur', width=550, height=320)
     return chart


</code>
"
3871,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Puducherry, Odisha, and Assam across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Puducherry', 'Odisha', 'Assam']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Puducherry', 'Odisha', 'Assam']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
3872,temporal_aggregation,Show the monthly average PM2.5 for Jalore in 2019 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Jalore') & (data['Timestamp'].dt.year == 2019)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Jalore 2019', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Jalore') & (data['Timestamp'].dt.year == 2019)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Jalore 2019', width=450, height=280)


</code>
"
3873,spatial_aggregation,Plot the top 12 states by average PM2.5 in 2024 as a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2024]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nlargest(12, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 12 States by Average PM2.5 in 2024', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2024]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nlargest(12, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 12 States by Average PM2.5 in 2024', width=500, height=300)
     return chart


</code>
"
3874,temporal_aggregation,Show the monthly average PM2.5 for Arrah in 2023 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Arrah') & (data['Timestamp'].dt.year == 2023)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Arrah 2023', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Arrah') & (data['Timestamp'].dt.year == 2023)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Arrah 2023', width=450, height=280)


</code>
"
3875,specific_pattern,Show a cumulative area chart of PM2.5 readings for Bulandshahr across 2019.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Bulandshahr') & (data['Timestamp'].dt.year == 2019)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Bulandshahr 2019', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Bulandshahr') & (data['Timestamp'].dt.year == 2019)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Bulandshahr 2019', width=600, height=300)
     return chart


</code>
"
3876,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Sikkim, Jharkhand, and Sikkim across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Sikkim', 'Jharkhand', 'Sikkim']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Sikkim', 'Jharkhand', 'Sikkim']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
3877,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Kerala, Chandigarh, and Tripura in 2019 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Kerala', 'Chandigarh', 'Tripura']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Kerala, Chandigarh, UP – 2019', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Kerala', 'Chandigarh', 'Tripura']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Kerala, Chandigarh, UP – 2019', width=550, height=320)
     return chart


</code>
"
3878,specific_pattern,Show a cumulative area chart of PM2.5 readings for Srinagar across 2021.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Srinagar') & (data['Timestamp'].dt.year == 2021)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Srinagar 2021', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Srinagar') & (data['Timestamp'].dt.year == 2021)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Srinagar 2021', width=600, height=300)
     return chart


</code>
"
3879,temporal_aggregation,Show the monthly average PM2.5 for Dhule in 2024 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Dhule') & (data['Timestamp'].dt.year == 2024)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Dhule 2024', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Dhule') & (data['Timestamp'].dt.year == 2024)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Dhule 2024', width=450, height=280)


</code>
"
3880,temporal_aggregation,Plot the weekly average PM2.5 for Varanasi in 2023 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Varanasi') & (data['Timestamp'].dt.year == 2023)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Varanasi 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Varanasi') & (data['Timestamp'].dt.year == 2023)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Varanasi 2023', width=600, height=300)
     return chart


</code>
"
3881,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Jharkhand, Tripura, and West Bengal from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Jharkhand', 'Tripura', 'West Bengal']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Jharkhand vs Tripura vs West Bengal', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Jharkhand', 'Tripura', 'West Bengal']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Jharkhand vs Tripura vs West Bengal', width=550, height=320)
     return chart


</code>
"
3882,temporal_aggregation,Show a monthly bar chart of the number of days Rajasthan exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2021.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Rajasthan') & (data['Timestamp'].dt.year == 2021)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Rajasthan Exceeded WHO PM2.5 Guideline per Month – 2021', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Rajasthan') & (data['Timestamp'].dt.year == 2021)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Rajasthan Exceeded WHO PM2.5 Guideline per Month – 2021', width=500, height=300)
     return chart


</code>
"
3883,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Maharashtra, Tripura, and Uttar Pradesh in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Maharashtra', 'Tripura', 'Uttar Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Maharashtra, Tripura, UP – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Maharashtra', 'Tripura', 'Uttar Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Maharashtra, Tripura, UP – 2024', width=550, height=320)
     return chart


</code>
"
3884,temporal_aggregation,Plot the weekly average PM2.5 for Bhiwadi in 2017 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Bhiwadi') & (data['Timestamp'].dt.year == 2017)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Bhiwadi 2017', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Bhiwadi') & (data['Timestamp'].dt.year == 2017)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Bhiwadi 2017', width=600, height=300)
     return chart


</code>
"
3885,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Tamil Nadu, Uttar Pradesh, and Mizoram across 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Tamil Nadu', 'Uttar Pradesh', 'Mizoram']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Tamil Nadu', 'Uttar Pradesh', 'Mizoram']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
     return chart


</code>
"
3886,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Tumakuru, Indore, and Angul in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Tumakuru', 'Indore', 'Angul']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Tumakuru vs Indore vs Angul – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Tumakuru', 'Indore', 'Angul']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Tumakuru vs Indore vs Angul – 2023', width=550, height=320)
     return chart


</code>
"
3887,temporal_aggregation,Plot the weekly average PM2.5 for Chengalpattu in 2023 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Chengalpattu') & (data['Timestamp'].dt.year == 2023)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Chengalpattu 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Chengalpattu') & (data['Timestamp'].dt.year == 2023)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Chengalpattu 2023', width=600, height=300)
     return chart


</code>
"
3888,temporal_aggregation,Plot the weekly average PM2.5 for Gummidipoondi in 2023 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Gummidipoondi') & (data['Timestamp'].dt.year == 2023)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Gummidipoondi 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Gummidipoondi') & (data['Timestamp'].dt.year == 2023)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Gummidipoondi 2023', width=600, height=300)
     return chart


</code>
"
3889,temporal_aggregation,Show the monthly average PM2.5 for Indore in 2017 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Indore') & (data['Timestamp'].dt.year == 2017)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Indore 2017', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Indore') & (data['Timestamp'].dt.year == 2017)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Indore 2017', width=450, height=280)


</code>
"
3890,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Chandigarh, Assam, and Puducherry across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Chandigarh', 'Assam', 'Puducherry']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Chandigarh', 'Assam', 'Puducherry']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
3891,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Rajasthan, Uttarakhand, and Madhya Pradesh across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Rajasthan', 'Uttarakhand', 'Madhya Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Rajasthan', 'Uttarakhand', 'Madhya Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
3892,temporal_aggregation,Plot the weekly average PM2.5 for Shivamogga in 2021 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Shivamogga') & (data['Timestamp'].dt.year == 2021)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Shivamogga 2021', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Shivamogga') & (data['Timestamp'].dt.year == 2021)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Shivamogga 2021', width=600, height=300)
     return chart


</code>
"
3893,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Arunachal Pradesh, Jharkhand, and Haryana from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Arunachal Pradesh', 'Jharkhand', 'Haryana']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Arunachal Pradesh vs Jharkhand vs Haryana', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Arunachal Pradesh', 'Jharkhand', 'Haryana']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Arunachal Pradesh vs Jharkhand vs Haryana', width=550, height=320)
     return chart


</code>
"
3894,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Parbhani, Sri Ganganagar, and Mandideep in 2019 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Parbhani', 'Sri Ganganagar', 'Mandideep']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Parbhani vs Sri Ganganagar vs Mandideep – 2019', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Parbhani', 'Sri Ganganagar', 'Mandideep']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Parbhani vs Sri Ganganagar vs Mandideep – 2019', width=550, height=320)
     return chart


</code>
"
3895,temporal_aggregation,Show the monthly average PM2.5 for Boisar in 2022 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Boisar') & (data['Timestamp'].dt.year == 2022)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Boisar 2022', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Boisar') & (data['Timestamp'].dt.year == 2022)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Boisar 2022', width=450, height=280)


</code>
"
3896,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Rajasthan, Karnataka, and Chandigarh in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Rajasthan', 'Karnataka', 'Chandigarh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Rajasthan, Karnataka, UP – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Rajasthan', 'Karnataka', 'Chandigarh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Rajasthan, Karnataka, UP – 2023', width=550, height=320)
     return chart


</code>
"
3897,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Thiruvananthapuram, Howrah, and Pathardih in 2017 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Thiruvananthapuram', 'Howrah', 'Pathardih']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2017)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Thiruvananthapuram vs Howrah vs Pathardih – 2017', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Thiruvananthapuram', 'Howrah', 'Pathardih']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2017)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Thiruvananthapuram vs Howrah vs Pathardih – 2017', width=550, height=320)
     return chart


</code>
"
3898,temporal_aggregation,Show the monthly average PM10 trend for Samastipur from 2017 to 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Samastipur'].copy()
    df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Samastipur (2017–2022)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Samastipur'].copy()
     df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Samastipur (2017–2022)', width=600, height=300)
     return chart


</code>
"
3899,temporal_aggregation,Show the monthly average PM2.5 for Visakhapatnam in 2020 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Visakhapatnam') & (data['Timestamp'].dt.year == 2020)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Visakhapatnam 2020', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Visakhapatnam') & (data['Timestamp'].dt.year == 2020)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Visakhapatnam 2020', width=450, height=280)


</code>
"
3900,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Puducherry, Haryana, and Jammu and Kashmir from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Puducherry', 'Haryana', 'Jammu and Kashmir']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Puducherry vs Haryana vs Jammu and Kashmir', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Puducherry', 'Haryana', 'Jammu and Kashmir']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Puducherry vs Haryana vs Jammu and Kashmir', width=550, height=320)
     return chart


</code>
"
3901,temporal_aggregation,Show the monthly average PM10 trend for Ariyalur from 2017 to 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Ariyalur'].copy()
    df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Ariyalur (2017–2022)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Ariyalur'].copy()
     df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Ariyalur (2017–2022)', width=600, height=300)
     return chart


</code>
"
3902,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Assam stations in 2020, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Assam') & (data['Timestamp'].dt.year == 2020)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Assam Stations 2020', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Assam') & (data['Timestamp'].dt.year == 2020)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Assam Stations 2020', width=450, height=350)


</code>
"
3903,temporal_aggregation,Plot the weekly average PM2.5 for Barbil in 2023 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Barbil') & (data['Timestamp'].dt.year == 2023)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Barbil 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Barbil') & (data['Timestamp'].dt.year == 2023)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Barbil 2023', width=600, height=300)
     return chart


</code>
"
3904,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Bikaner, Thane, and Howrah in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Bikaner', 'Thane', 'Howrah']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Bikaner vs Thane vs Howrah – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Bikaner', 'Thane', 'Howrah']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Bikaner vs Thane vs Howrah – 2024', width=550, height=320)
     return chart


</code>
"
3905,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Gujarat, Assam, and Chandigarh across 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Gujarat', 'Assam', 'Chandigarh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Gujarat', 'Assam', 'Chandigarh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
     return chart


</code>
"
3906,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Haryana, Madhya Pradesh, and Delhi in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Haryana', 'Madhya Pradesh', 'Delhi']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Haryana, Madhya Pradesh, UP – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Haryana', 'Madhya Pradesh', 'Delhi']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Haryana, Madhya Pradesh, UP – 2024', width=550, height=320)
     return chart


</code>
"
3907,temporal_aggregation,Plot the weekly average PM2.5 for Chikkamagaluru in 2020 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Chikkamagaluru') & (data['Timestamp'].dt.year == 2020)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Chikkamagaluru 2020', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Chikkamagaluru') & (data['Timestamp'].dt.year == 2020)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Chikkamagaluru 2020', width=600, height=300)
     return chart


</code>
"
3908,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Odisha, Karnataka, and Mizoram in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Odisha', 'Karnataka', 'Mizoram']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Odisha, Karnataka, UP – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Odisha', 'Karnataka', 'Mizoram']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Odisha, Karnataka, UP – 2023', width=550, height=320)
     return chart


</code>
"
3909,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Bihar, Kerala, and Telangana across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Bihar', 'Kerala', 'Telangana']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Bihar', 'Kerala', 'Telangana']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
3910,temporal_aggregation,Show the monthly average PM10 trend for Mangalore from 2017 to 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Mangalore'].copy()
    df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Mangalore (2017–2022)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Mangalore'].copy()
     df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Mangalore (2017–2022)', width=600, height=300)
     return chart


</code>
"
3911,specific_pattern,Plot the rolling 30-day average PM2.5 for Telangana in 2023 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Telangana') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Telangana 2023', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Telangana') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Telangana 2023', width=600, height=300)


</code>
"
3912,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Mizoram, Meghalaya, and Mizoram across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Mizoram', 'Meghalaya', 'Mizoram']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Mizoram', 'Meghalaya', 'Mizoram']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
3913,temporal_aggregation,Plot the weekly average PM2.5 for Sagar in 2020 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Sagar') & (data['Timestamp'].dt.year == 2020)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Sagar 2020', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Sagar') & (data['Timestamp'].dt.year == 2020)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Sagar 2020', width=600, height=300)
     return chart


</code>
"
3914,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Mumbai, Davanagere, and Samastipur in 2017 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Mumbai', 'Davanagere', 'Samastipur']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2017)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Mumbai vs Davanagere vs Samastipur – 2017', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Mumbai', 'Davanagere', 'Samastipur']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2017)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Mumbai vs Davanagere vs Samastipur – 2017', width=550, height=320)
     return chart


</code>
"
3915,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Raichur, Ghaziabad, and Mira-Bhayandar in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Raichur', 'Ghaziabad', 'Mira-Bhayandar']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Raichur vs Ghaziabad vs Mira-Bhayandar – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Raichur', 'Ghaziabad', 'Mira-Bhayandar']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Raichur vs Ghaziabad vs Mira-Bhayandar – 2024', width=550, height=320)
     return chart


</code>
"
3916,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Chandigarh, Odisha, and Telangana from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Chandigarh', 'Odisha', 'Telangana']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Chandigarh vs Odisha vs Telangana', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Chandigarh', 'Odisha', 'Telangana']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Chandigarh vs Odisha vs Telangana', width=550, height=320)
     return chart


</code>
"
3917,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Yadgir, Rajamahendravaram, and Araria in 2020 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Yadgir', 'Rajamahendravaram', 'Araria']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2020)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Yadgir vs Rajamahendravaram vs Araria – 2020', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Yadgir', 'Rajamahendravaram', 'Araria']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2020)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Yadgir vs Rajamahendravaram vs Araria – 2020', width=550, height=320)
     return chart


</code>
"
3918,temporal_aggregation,Show the monthly average PM10 trend for Bareilly from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Bareilly'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Bareilly (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Bareilly'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Bareilly (2019–2024)', width=600, height=300)
     return chart


</code>
"
3919,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Rajasthan, Punjab, and Puducherry from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Rajasthan', 'Punjab', 'Puducherry']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Rajasthan vs Punjab vs Puducherry', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Rajasthan', 'Punjab', 'Puducherry']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Rajasthan vs Punjab vs Puducherry', width=550, height=320)
     return chart


</code>
"
3920,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Nagaland, Odisha, and Manipur in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Nagaland', 'Odisha', 'Manipur']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Nagaland, Odisha, UP – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Nagaland', 'Odisha', 'Manipur']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Nagaland, Odisha, UP – 2023', width=550, height=320)
     return chart


</code>
"
3921,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Malegaon, Kadapa, and Patna in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Malegaon', 'Kadapa', 'Patna']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Malegaon vs Kadapa vs Patna – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Malegaon', 'Kadapa', 'Patna']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Malegaon vs Kadapa vs Patna – 2022', width=550, height=320)
     return chart


</code>
"
3922,temporal_aggregation,Show the monthly average PM10 trend for Nashik from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Nashik'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Nashik (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Nashik'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Nashik (2019–2024)', width=600, height=300)
     return chart


</code>
"
3923,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Manipur, West Bengal, and Assam from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Manipur', 'West Bengal', 'Assam']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Manipur vs West Bengal vs Assam', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Manipur', 'West Bengal', 'Assam']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Manipur vs West Bengal vs Assam', width=550, height=320)
     return chart


</code>
"
3924,temporal_aggregation,Show the monthly average PM10 trend for Tumakuru from 2017 to 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Tumakuru'].copy()
    df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Tumakuru (2017–2022)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Tumakuru'].copy()
     df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Tumakuru (2017–2022)', width=600, height=300)
     return chart


</code>
"
3925,temporal_aggregation,Plot the weekly average PM2.5 for Gwalior in 2021 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Gwalior') & (data['Timestamp'].dt.year == 2021)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Gwalior 2021', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Gwalior') & (data['Timestamp'].dt.year == 2021)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Gwalior 2021', width=600, height=300)
     return chart


</code>
"
3926,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Gangtok, Rohtak, and Hubballi in 2018 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Gangtok', 'Rohtak', 'Hubballi']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2018)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Gangtok vs Rohtak vs Hubballi – 2018', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Gangtok', 'Rohtak', 'Hubballi']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2018)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Gangtok vs Rohtak vs Hubballi – 2018', width=550, height=320)
     return chart


</code>
"
3927,temporal_aggregation,Show the monthly average PM2.5 for Jhalawar in 2023 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Jhalawar') & (data['Timestamp'].dt.year == 2023)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Jhalawar 2023', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Jhalawar') & (data['Timestamp'].dt.year == 2023)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Jhalawar 2023', width=450, height=280)


</code>
"
3928,specific_pattern,Show a cumulative area chart of PM2.5 readings for Narnaul across 2022.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Narnaul') & (data['Timestamp'].dt.year == 2022)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Narnaul 2022', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Narnaul') & (data['Timestamp'].dt.year == 2022)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Narnaul 2022', width=600, height=300)
     return chart


</code>
"
3929,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Jharkhand, Andhra Pradesh, and Chhattisgarh in 2017 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Jharkhand', 'Andhra Pradesh', 'Chhattisgarh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2017)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Jharkhand, Andhra Pradesh, UP – 2017', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Jharkhand', 'Andhra Pradesh', 'Chhattisgarh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2017)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Jharkhand, Andhra Pradesh, UP – 2017', width=550, height=320)
     return chart


</code>
"
3930,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Andhra Pradesh, Tamil Nadu, and Puducherry from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Andhra Pradesh', 'Tamil Nadu', 'Puducherry']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Andhra Pradesh vs Tamil Nadu vs Puducherry', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Andhra Pradesh', 'Tamil Nadu', 'Puducherry']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Andhra Pradesh vs Tamil Nadu vs Puducherry', width=550, height=320)
     return chart


</code>
"
3931,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Madhya Pradesh, Telangana, and Tripura from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Madhya Pradesh', 'Telangana', 'Tripura']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Madhya Pradesh vs Telangana vs Tripura', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Madhya Pradesh', 'Telangana', 'Tripura']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Madhya Pradesh vs Telangana vs Tripura', width=550, height=320)
     return chart


</code>
"
3932,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Bihar, Nagaland, and Chandigarh from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Bihar', 'Nagaland', 'Chandigarh']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Bihar vs Nagaland vs Chandigarh', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Bihar', 'Nagaland', 'Chandigarh']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Bihar vs Nagaland vs Chandigarh', width=550, height=320)
     return chart


</code>
"
3933,specific_pattern,Show a cumulative area chart of PM2.5 readings for Tirupati across 2017.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Tirupati') & (data['Timestamp'].dt.year == 2017)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Tirupati 2017', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Tirupati') & (data['Timestamp'].dt.year == 2017)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Tirupati 2017', width=600, height=300)
     return chart


</code>
"
3934,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Punjab, Odisha, and Jammu and Kashmir in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Punjab', 'Odisha', 'Jammu and Kashmir']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Punjab, Odisha, UP – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Punjab', 'Odisha', 'Jammu and Kashmir']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Punjab, Odisha, UP – 2022', width=550, height=320)
     return chart


</code>
"
3935,temporal_aggregation,Show the monthly average PM2.5 for Vijayapura in 2017 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Vijayapura') & (data['Timestamp'].dt.year == 2017)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Vijayapura 2017', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Vijayapura') & (data['Timestamp'].dt.year == 2017)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Vijayapura 2017', width=450, height=280)


</code>
"
3936,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Uttar Pradesh, Himachal Pradesh, and Nagaland across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Uttar Pradesh', 'Himachal Pradesh', 'Nagaland']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Uttar Pradesh', 'Himachal Pradesh', 'Nagaland']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
3937,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Uttarakhand, Arunachal Pradesh, and Andhra Pradesh from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Uttarakhand', 'Arunachal Pradesh', 'Andhra Pradesh']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Uttarakhand vs Arunachal Pradesh vs Andhra Pradesh', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Uttarakhand', 'Arunachal Pradesh', 'Andhra Pradesh']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Uttarakhand vs Arunachal Pradesh vs Andhra Pradesh', width=550, height=320)
     return chart


</code>
"
3938,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Nagaland, Kerala, and Himachal Pradesh in 2021 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Nagaland', 'Kerala', 'Himachal Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Nagaland, Kerala, UP – 2021', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Nagaland', 'Kerala', 'Himachal Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Nagaland, Kerala, UP – 2021', width=550, height=320)
     return chart


</code>
"
3939,temporal_aggregation,Show the monthly average PM2.5 for Katihar in 2024 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Katihar') & (data['Timestamp'].dt.year == 2024)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Katihar 2024', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Katihar') & (data['Timestamp'].dt.year == 2024)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Katihar 2024', width=450, height=280)


</code>
"
3940,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Visakhapatnam, Virar, and Rajgir in 2017 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Visakhapatnam', 'Virar', 'Rajgir']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2017)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Visakhapatnam vs Virar vs Rajgir – 2017', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Visakhapatnam', 'Virar', 'Rajgir']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2017)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Visakhapatnam vs Virar vs Rajgir – 2017', width=550, height=320)
     return chart


</code>
"
3941,specific_pattern,Show a cumulative area chart of PM2.5 readings for Mumbai across 2024.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Mumbai') & (data['Timestamp'].dt.year == 2024)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Mumbai 2024', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Mumbai') & (data['Timestamp'].dt.year == 2024)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Mumbai 2024', width=600, height=300)
     return chart


</code>
"
3942,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Odisha, Kerala, and Chandigarh across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Odisha', 'Kerala', 'Chandigarh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Odisha', 'Kerala', 'Chandigarh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
3943,temporal_aggregation,Plot the weekly average PM2.5 for Satna in 2020 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Satna') & (data['Timestamp'].dt.year == 2020)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Satna 2020', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Satna') & (data['Timestamp'].dt.year == 2020)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Satna 2020', width=600, height=300)
     return chart


</code>
"
3944,temporal_aggregation,Plot the weekly average PM2.5 for Bulandshahr in 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Bulandshahr') & (data['Timestamp'].dt.year == 2024)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Bulandshahr 2024', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Bulandshahr') & (data['Timestamp'].dt.year == 2024)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Bulandshahr 2024', width=600, height=300)
     return chart


</code>
"
3945,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Jharkhand, Nagaland, and Puducherry across 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Jharkhand', 'Nagaland', 'Puducherry']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Jharkhand', 'Nagaland', 'Puducherry']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
     return chart


</code>
"
3946,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Alwar, Varanasi, and Rairangpur in 2018 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Alwar', 'Varanasi', 'Rairangpur']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2018)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Alwar vs Varanasi vs Rairangpur – 2018', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Alwar', 'Varanasi', 'Rairangpur']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2018)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Alwar vs Varanasi vs Rairangpur – 2018', width=550, height=320)
     return chart


</code>
"
3947,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Sikkim, Telangana, and Arunachal Pradesh across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Sikkim', 'Telangana', 'Arunachal Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Sikkim', 'Telangana', 'Arunachal Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
3948,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Puducherry, Rajasthan, and West Bengal from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Puducherry', 'Rajasthan', 'West Bengal']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Puducherry vs Rajasthan vs West Bengal', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Puducherry', 'Rajasthan', 'West Bengal']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Puducherry vs Rajasthan vs West Bengal', width=550, height=320)
     return chart


</code>
"
3949,temporal_aggregation,Plot the weekly average PM2.5 for Solapur in 2019 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Solapur') & (data['Timestamp'].dt.year == 2019)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Solapur 2019', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Solapur') & (data['Timestamp'].dt.year == 2019)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Solapur 2019', width=600, height=300)
     return chart


</code>
"
3950,temporal_aggregation,Show the monthly average PM2.5 for Belgaum in 2019 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Belgaum') & (data['Timestamp'].dt.year == 2019)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Belgaum 2019', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Belgaum') & (data['Timestamp'].dt.year == 2019)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Belgaum 2019', width=450, height=280)


</code>
"
3951,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Karnataka, Odisha, and Jammu and Kashmir in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Karnataka', 'Odisha', 'Jammu and Kashmir']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Karnataka, Odisha, UP – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Karnataka', 'Odisha', 'Jammu and Kashmir']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Karnataka, Odisha, UP – 2024', width=550, height=320)
     return chart


</code>
"
3952,temporal_aggregation,Show the monthly average PM2.5 for Thanjavur in 2018 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Thanjavur') & (data['Timestamp'].dt.year == 2018)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Thanjavur 2018', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Thanjavur') & (data['Timestamp'].dt.year == 2018)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Thanjavur 2018', width=450, height=280)


</code>
"
3953,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Arunachal Pradesh, Madhya Pradesh, and Tamil Nadu in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Arunachal Pradesh', 'Madhya Pradesh', 'Tamil Nadu']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Arunachal Pradesh, Madhya Pradesh, UP – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Arunachal Pradesh', 'Madhya Pradesh', 'Tamil Nadu']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Arunachal Pradesh, Madhya Pradesh, UP – 2024', width=550, height=320)
     return chart


</code>
"
3954,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Delhi, Bhilai, and Dhule in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Delhi', 'Bhilai', 'Dhule']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Delhi vs Bhilai vs Dhule – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Delhi', 'Bhilai', 'Dhule']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Delhi vs Bhilai vs Dhule – 2023', width=550, height=320)
     return chart


</code>
"
3955,temporal_aggregation,Show the monthly average PM10 trend for Hassan from 2017 to 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Hassan'].copy()
    df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Hassan (2017–2022)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Hassan'].copy()
     df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Hassan (2017–2022)', width=600, height=300)
     return chart


</code>
"
3956,specific_pattern,Show a cumulative area chart of PM2.5 readings for Chandigarh across 2024.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Chandigarh') & (data['Timestamp'].dt.year == 2024)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Chandigarh 2024', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Chandigarh') & (data['Timestamp'].dt.year == 2024)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Chandigarh 2024', width=600, height=300)
     return chart


</code>
"
3957,specific_pattern,Show a cumulative area chart of PM2.5 readings for Navi Mumbai across 2021.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Navi Mumbai') & (data['Timestamp'].dt.year == 2021)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Navi Mumbai 2021', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Navi Mumbai') & (data['Timestamp'].dt.year == 2021)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Navi Mumbai 2021', width=600, height=300)
     return chart


</code>
"
3958,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Mizoram, Uttar Pradesh, and Assam from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Mizoram', 'Uttar Pradesh', 'Assam']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Mizoram vs Uttar Pradesh vs Assam', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Mizoram', 'Uttar Pradesh', 'Assam']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Mizoram vs Uttar Pradesh vs Assam', width=550, height=320)
     return chart


</code>
"
3959,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Tripura, Haryana, and Tripura across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Tripura', 'Haryana', 'Tripura']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Tripura', 'Haryana', 'Tripura']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
3960,temporal_aggregation,Show the monthly average PM10 trend for Korba from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Korba'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Korba (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Korba'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Korba (2019–2024)', width=600, height=300)
     return chart


</code>
"
3961,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Odisha, Kerala, and Manipur from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Odisha', 'Kerala', 'Manipur']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Odisha vs Kerala vs Manipur', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Odisha', 'Kerala', 'Manipur']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Odisha vs Kerala vs Manipur', width=550, height=320)
     return chart


</code>
"
3962,temporal_aggregation,Show the monthly average PM2.5 for Aurangabad in 2022 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Aurangabad') & (data['Timestamp'].dt.year == 2022)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Aurangabad 2022', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Aurangabad') & (data['Timestamp'].dt.year == 2022)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Aurangabad 2022', width=450, height=280)


</code>
"
3963,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Andhra Pradesh, Telangana, and Uttar Pradesh in 2018 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Andhra Pradesh', 'Telangana', 'Uttar Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2018)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Andhra Pradesh, Telangana, UP – 2018', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Andhra Pradesh', 'Telangana', 'Uttar Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2018)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Andhra Pradesh, Telangana, UP – 2018', width=550, height=320)
     return chart


</code>
"
3964,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Mandikhera, Latur, and Tumakuru in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Mandikhera', 'Latur', 'Tumakuru']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Mandikhera vs Latur vs Tumakuru – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Mandikhera', 'Latur', 'Tumakuru']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Mandikhera vs Latur vs Tumakuru – 2022', width=550, height=320)
     return chart


</code>
"
3965,temporal_aggregation,Show the monthly average PM10 trend for Moradabad from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Moradabad'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Moradabad (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Moradabad'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Moradabad (2019–2024)', width=600, height=300)
     return chart


</code>
"
3966,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Karnataka, Jharkhand, and West Bengal in 2019 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Karnataka', 'Jharkhand', 'West Bengal']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Karnataka, Jharkhand, UP – 2019', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Karnataka', 'Jharkhand', 'West Bengal']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Karnataka, Jharkhand, UP – 2019', width=550, height=320)
     return chart


</code>
"
3967,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Uttar Pradesh, Puducherry, and Gujarat across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Uttar Pradesh', 'Puducherry', 'Gujarat']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Uttar Pradesh', 'Puducherry', 'Gujarat']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
3968,temporal_aggregation,Plot the weekly average PM2.5 for Ulhasnagar in 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Ulhasnagar') & (data['Timestamp'].dt.year == 2024)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Ulhasnagar 2024', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Ulhasnagar') & (data['Timestamp'].dt.year == 2024)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Ulhasnagar 2024', width=600, height=300)
     return chart


</code>
"
3969,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Haryana, Kerala, and Tripura across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Haryana', 'Kerala', 'Tripura']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Haryana', 'Kerala', 'Tripura']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
3970,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Mizoram, Telangana, and Rajasthan across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Mizoram', 'Telangana', 'Rajasthan']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Mizoram', 'Telangana', 'Rajasthan']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
3971,specific_pattern,Show a cumulative area chart of PM2.5 readings for Damoh across 2018.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Damoh') & (data['Timestamp'].dt.year == 2018)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Damoh 2018', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Damoh') & (data['Timestamp'].dt.year == 2018)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Damoh 2018', width=600, height=300)
     return chart


</code>
"
3972,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Puducherry, Delhi, and Jharkhand in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Puducherry', 'Delhi', 'Jharkhand']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Puducherry, Delhi, UP – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Puducherry', 'Delhi', 'Jharkhand']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Puducherry, Delhi, UP – 2022', width=550, height=320)
     return chart


</code>
"
3973,specific_pattern,Plot the rolling 30-day average PM2.5 for Assam in 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Assam') & (data['Timestamp'].dt.year == 2022)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Assam 2022', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Assam') & (data['Timestamp'].dt.year == 2022)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Assam 2022', width=600, height=300)


</code>
"
3974,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Maharashtra, Odisha, and Jammu and Kashmir in 2017 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Maharashtra', 'Odisha', 'Jammu and Kashmir']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2017)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Maharashtra, Odisha, UP – 2017', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Maharashtra', 'Odisha', 'Jammu and Kashmir']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2017)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Maharashtra, Odisha, UP – 2017', width=550, height=320)
     return chart


</code>
"
3975,temporal_aggregation,Plot the weekly average PM2.5 for Jalna in 2023 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Jalna') & (data['Timestamp'].dt.year == 2023)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Jalna 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Jalna') & (data['Timestamp'].dt.year == 2023)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Jalna 2023', width=600, height=300)
     return chart


</code>
"
3976,temporal_aggregation,Show the monthly average PM10 trend for Kanchipuram from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Kanchipuram'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Kanchipuram (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Kanchipuram'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Kanchipuram (2019–2024)', width=600, height=300)
     return chart


</code>
"
3977,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Punjab, Jammu and Kashmir, and Gujarat in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Punjab', 'Jammu and Kashmir', 'Gujarat']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Punjab, Jammu and Kashmir, UP – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Punjab', 'Jammu and Kashmir', 'Gujarat']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Punjab, Jammu and Kashmir, UP – 2023', width=550, height=320)
     return chart


</code>
"
3978,temporal_aggregation,Show the monthly average PM10 trend for Sawai Madhopur from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Sawai Madhopur'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Sawai Madhopur (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Sawai Madhopur'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Sawai Madhopur (2019–2024)', width=600, height=300)
     return chart


</code>
"
3979,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Jharkhand, Jharkhand, and Kerala from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Jharkhand', 'Jharkhand', 'Kerala']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Jharkhand vs Jharkhand vs Kerala', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Jharkhand', 'Jharkhand', 'Kerala']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Jharkhand vs Jharkhand vs Kerala', width=550, height=320)
     return chart


</code>
"
3980,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Jammu and Kashmir, Puducherry, and Uttar Pradesh across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Jammu and Kashmir', 'Puducherry', 'Uttar Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Jammu and Kashmir', 'Puducherry', 'Uttar Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
3981,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Tamil Nadu, Delhi, and Sikkim in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Tamil Nadu', 'Delhi', 'Sikkim']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Tamil Nadu, Delhi, UP – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Tamil Nadu', 'Delhi', 'Sikkim']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Tamil Nadu, Delhi, UP – 2022', width=550, height=320)
     return chart


</code>
"
3982,temporal_aggregation,Show the monthly average PM10 trend for Durgapur from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Durgapur'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Durgapur (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Durgapur'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Durgapur (2019–2024)', width=600, height=300)
     return chart


</code>
"
3983,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Balasore, Jalore, and Haveri in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Balasore', 'Jalore', 'Haveri']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Balasore vs Jalore vs Haveri – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Balasore', 'Jalore', 'Haveri']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Balasore vs Jalore vs Haveri – 2024', width=550, height=320)
     return chart


</code>
"
3984,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Tripura, Tripura, and Nagaland across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Tripura', 'Tripura', 'Nagaland']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Tripura', 'Tripura', 'Nagaland']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
3985,temporal_aggregation,Show the monthly average PM2.5 for Yamuna Nagar in 2018 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Yamuna Nagar') & (data['Timestamp'].dt.year == 2018)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Yamuna Nagar 2018', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Yamuna Nagar') & (data['Timestamp'].dt.year == 2018)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Yamuna Nagar 2018', width=450, height=280)


</code>
"
3986,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Hanumangarh, Jhalawar, and Noida in 2018 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Hanumangarh', 'Jhalawar', 'Noida']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2018)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Hanumangarh vs Jhalawar vs Noida – 2018', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Hanumangarh', 'Jhalawar', 'Noida']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2018)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Hanumangarh vs Jhalawar vs Noida – 2018', width=550, height=320)
     return chart


</code>
"
3987,temporal_aggregation,Plot the weekly average PM2.5 for Delhi in 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Delhi') & (data['Timestamp'].dt.year == 2024)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Delhi 2024', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Delhi') & (data['Timestamp'].dt.year == 2024)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Delhi 2024', width=600, height=300)
     return chart


</code>
"
3988,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Assam stations in 2021, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Assam') & (data['Timestamp'].dt.year == 2021)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Assam Stations 2021', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Assam') & (data['Timestamp'].dt.year == 2021)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Assam Stations 2021', width=450, height=350)


</code>
"
3989,temporal_aggregation,Show a monthly bar chart of the number of days Karnataka exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2021.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Karnataka') & (data['Timestamp'].dt.year == 2021)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Karnataka Exceeded WHO PM2.5 Guideline per Month – 2021', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Karnataka') & (data['Timestamp'].dt.year == 2021)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Karnataka Exceeded WHO PM2.5 Guideline per Month – 2021', width=500, height=300)
     return chart


</code>
"
3990,temporal_aggregation,Show the monthly average PM2.5 for Nagapattinam in 2022 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Nagapattinam') & (data['Timestamp'].dt.year == 2022)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Nagapattinam 2022', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Nagapattinam') & (data['Timestamp'].dt.year == 2022)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Nagapattinam 2022', width=450, height=280)


</code>
"
3991,temporal_aggregation,Plot the weekly average PM2.5 for Alwar in 2017 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Alwar') & (data['Timestamp'].dt.year == 2017)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Alwar 2017', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Alwar') & (data['Timestamp'].dt.year == 2017)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Alwar 2017', width=600, height=300)
     return chart


</code>
"
3992,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Odisha, Jammu and Kashmir, and Chandigarh across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Odisha', 'Jammu and Kashmir', 'Chandigarh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Odisha', 'Jammu and Kashmir', 'Chandigarh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
3993,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Uttarakhand, Uttar Pradesh, and Madhya Pradesh across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Uttarakhand', 'Uttar Pradesh', 'Madhya Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Uttarakhand', 'Uttar Pradesh', 'Madhya Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
3994,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Chhattisgarh, Himachal Pradesh, and Punjab from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Chhattisgarh', 'Himachal Pradesh', 'Punjab']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Chhattisgarh vs Himachal Pradesh vs Punjab', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Chhattisgarh', 'Himachal Pradesh', 'Punjab']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Chhattisgarh vs Himachal Pradesh vs Punjab', width=550, height=320)
     return chart


</code>
"
3995,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Aizawl, Bhiwandi, and Chittoor in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Aizawl', 'Bhiwandi', 'Chittoor']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Aizawl vs Bhiwandi vs Chittoor – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Aizawl', 'Bhiwandi', 'Chittoor']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Aizawl vs Bhiwandi vs Chittoor – 2023', width=550, height=320)
     return chart


</code>
"
3996,temporal_aggregation,Show the monthly average PM10 trend for Bhopal from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Bhopal'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Bhopal (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Bhopal'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Bhopal (2019–2024)', width=600, height=300)
     return chart


</code>
"
3997,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Mysuru, Shivamogga, and Milupara in 2019 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Mysuru', 'Shivamogga', 'Milupara']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Mysuru vs Shivamogga vs Milupara – 2019', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Mysuru', 'Shivamogga', 'Milupara']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Mysuru vs Shivamogga vs Milupara – 2019', width=550, height=320)
     return chart


</code>
"
3998,temporal_aggregation,Show the monthly average PM2.5 for Chennai in 2020 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Chennai') & (data['Timestamp'].dt.year == 2020)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Chennai 2020', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Chennai') & (data['Timestamp'].dt.year == 2020)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Chennai 2020', width=450, height=280)


</code>
"
3999,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Kerala, Kerala, and Karnataka from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Kerala', 'Kerala', 'Karnataka']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Kerala vs Kerala vs Karnataka', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Kerala', 'Kerala', 'Karnataka']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Kerala vs Kerala vs Karnataka', width=550, height=320)
     return chart


</code>
"
4000,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Delhi, Chhattisgarh, and Arunachal Pradesh across 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Delhi', 'Chhattisgarh', 'Arunachal Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Delhi', 'Chhattisgarh', 'Arunachal Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
     return chart


</code>
"
4001,temporal_aggregation,Show the monthly average PM2.5 for Cuttack in 2019 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Cuttack') & (data['Timestamp'].dt.year == 2019)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Cuttack 2019', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Cuttack') & (data['Timestamp'].dt.year == 2019)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Cuttack 2019', width=450, height=280)


</code>
"
4002,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Nagaland, Himachal Pradesh, and Nagaland across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Nagaland', 'Himachal Pradesh', 'Nagaland']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Nagaland', 'Himachal Pradesh', 'Nagaland']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
4003,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Puducherry, Mizoram, and West Bengal across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Puducherry', 'Mizoram', 'West Bengal']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Puducherry', 'Mizoram', 'West Bengal']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
4004,temporal_aggregation,Show a monthly bar chart of the number of days Gujarat exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2021.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Gujarat') & (data['Timestamp'].dt.year == 2021)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Gujarat Exceeded WHO PM2.5 Guideline per Month – 2021', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Gujarat') & (data['Timestamp'].dt.year == 2021)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Gujarat Exceeded WHO PM2.5 Guideline per Month – 2021', width=500, height=300)
     return chart


</code>
"
4005,temporal_aggregation,Show the monthly average PM2.5 for Jaisalmer in 2024 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Jaisalmer') & (data['Timestamp'].dt.year == 2024)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Jaisalmer 2024', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Jaisalmer') & (data['Timestamp'].dt.year == 2024)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Jaisalmer 2024', width=450, height=280)


</code>
"
4006,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Perundurai, Pune, and Jhalawar in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Perundurai', 'Pune', 'Jhalawar']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Perundurai vs Pune vs Jhalawar – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Perundurai', 'Pune', 'Jhalawar']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Perundurai vs Pune vs Jhalawar – 2023', width=550, height=320)
     return chart


</code>
"
4007,temporal_aggregation,Show a monthly bar chart of the number of days Telangana exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2022.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Telangana') & (data['Timestamp'].dt.year == 2022)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Telangana Exceeded WHO PM2.5 Guideline per Month – 2022', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Telangana') & (data['Timestamp'].dt.year == 2022)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Telangana Exceeded WHO PM2.5 Guideline per Month – 2022', width=500, height=300)
     return chart


</code>
"
4008,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Kerala, Assam, and Tamil Nadu across 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Kerala', 'Assam', 'Tamil Nadu']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Kerala', 'Assam', 'Tamil Nadu']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
     return chart


</code>
"
4009,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Puducherry, Haryana, and Himachal Pradesh from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Puducherry', 'Haryana', 'Himachal Pradesh']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Puducherry vs Haryana vs Himachal Pradesh', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Puducherry', 'Haryana', 'Himachal Pradesh']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Puducherry vs Haryana vs Himachal Pradesh', width=550, height=320)
     return chart


</code>
"
4010,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Punjab, Gujarat, and Odisha from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Punjab', 'Gujarat', 'Odisha']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Punjab vs Gujarat vs Odisha', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Punjab', 'Gujarat', 'Odisha']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Punjab vs Gujarat vs Odisha', width=550, height=320)
     return chart


</code>
"
4011,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Bihar, Andhra Pradesh, and Karnataka in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Bihar', 'Andhra Pradesh', 'Karnataka']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Bihar, Andhra Pradesh, UP – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Bihar', 'Andhra Pradesh', 'Karnataka']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Bihar, Andhra Pradesh, UP – 2022', width=550, height=320)
     return chart


</code>
"
4012,temporal_aggregation,Show the monthly average PM2.5 for Sangli in 2019 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Sangli') & (data['Timestamp'].dt.year == 2019)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Sangli 2019', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Sangli') & (data['Timestamp'].dt.year == 2019)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Sangli 2019', width=450, height=280)


</code>
"
4013,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Meghalaya, Nagaland, and Tamil Nadu in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Meghalaya', 'Nagaland', 'Tamil Nadu']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Meghalaya, Nagaland, UP – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Meghalaya', 'Nagaland', 'Tamil Nadu']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Meghalaya, Nagaland, UP – 2023', width=550, height=320)
     return chart


</code>
"
4014,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Jharkhand, Bihar, and Chandigarh in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Jharkhand', 'Bihar', 'Chandigarh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Jharkhand, Bihar, UP – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Jharkhand', 'Bihar', 'Chandigarh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Jharkhand, Bihar, UP – 2022', width=550, height=320)
     return chart


</code>
"
4015,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Karnataka, Telangana, and Odisha in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Karnataka', 'Telangana', 'Odisha']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Karnataka, Telangana, UP – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Karnataka', 'Telangana', 'Odisha']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Karnataka, Telangana, UP – 2024', width=550, height=320)
     return chart


</code>
"
4016,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Chhattisgarh, Telangana, and Arunachal Pradesh across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Chhattisgarh', 'Telangana', 'Arunachal Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Chhattisgarh', 'Telangana', 'Arunachal Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
4017,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Sikkim, Puducherry, and Madhya Pradesh in 2017 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Sikkim', 'Puducherry', 'Madhya Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2017)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Sikkim, Puducherry, UP – 2017', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Sikkim', 'Puducherry', 'Madhya Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2017)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Sikkim, Puducherry, UP – 2017', width=550, height=320)
     return chart


</code>
"
4018,temporal_aggregation,Plot the weekly average PM2.5 for Dindigul in 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Dindigul') & (data['Timestamp'].dt.year == 2024)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Dindigul 2024', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Dindigul') & (data['Timestamp'].dt.year == 2024)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Dindigul 2024', width=600, height=300)
     return chart


</code>
"
4019,temporal_aggregation,Plot the weekly average PM2.5 for Tensa in 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Tensa') & (data['Timestamp'].dt.year == 2024)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Tensa 2024', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Tensa') & (data['Timestamp'].dt.year == 2024)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Tensa 2024', width=600, height=300)
     return chart


</code>
"
4020,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Bihar, Odisha, and Madhya Pradesh across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Bihar', 'Odisha', 'Madhya Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Bihar', 'Odisha', 'Madhya Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
4021,specific_pattern,Show a cumulative area chart of PM2.5 readings for Ratlam across 2022.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Ratlam') & (data['Timestamp'].dt.year == 2022)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Ratlam 2022', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Ratlam') & (data['Timestamp'].dt.year == 2022)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Ratlam 2022', width=600, height=300)
     return chart


</code>
"
4022,temporal_aggregation,Show the monthly average PM2.5 for Buxar in 2019 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Buxar') & (data['Timestamp'].dt.year == 2019)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Buxar 2019', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Buxar') & (data['Timestamp'].dt.year == 2019)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Buxar 2019', width=450, height=280)


</code>
"
4023,specific_pattern,Show a cumulative area chart of PM2.5 readings for Varanasi across 2017.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Varanasi') & (data['Timestamp'].dt.year == 2017)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Varanasi 2017', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Varanasi') & (data['Timestamp'].dt.year == 2017)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Varanasi 2017', width=600, height=300)
     return chart


</code>
"
4024,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Bihar, Madhya Pradesh, and Kerala from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Bihar', 'Madhya Pradesh', 'Kerala']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Bihar vs Madhya Pradesh vs Kerala', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Bihar', 'Madhya Pradesh', 'Kerala']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Bihar vs Madhya Pradesh vs Kerala', width=550, height=320)
     return chart


</code>
"
4025,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Tamil Nadu, Rajasthan, and Telangana across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Tamil Nadu', 'Rajasthan', 'Telangana']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Tamil Nadu', 'Rajasthan', 'Telangana']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
4026,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Rajgir, Purnia, and Dharwad in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Rajgir', 'Purnia', 'Dharwad']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Rajgir vs Purnia vs Dharwad – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Rajgir', 'Purnia', 'Dharwad']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Rajgir vs Purnia vs Dharwad – 2024', width=550, height=320)
     return chart


</code>
"
4027,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Sikkim, Chhattisgarh, and Andhra Pradesh from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Sikkim', 'Chhattisgarh', 'Andhra Pradesh']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Sikkim vs Chhattisgarh vs Andhra Pradesh', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Sikkim', 'Chhattisgarh', 'Andhra Pradesh']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Sikkim vs Chhattisgarh vs Andhra Pradesh', width=550, height=320)
     return chart


</code>
"
4028,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Balasore, Sirsa, and Ratlam in 2020 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Balasore', 'Sirsa', 'Ratlam']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2020)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Balasore vs Sirsa vs Ratlam – 2020', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Balasore', 'Sirsa', 'Ratlam']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2020)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Balasore vs Sirsa vs Ratlam – 2020', width=550, height=320)
     return chart


</code>
"
4029,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Andhra Pradesh, Madhya Pradesh, and Odisha from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Andhra Pradesh', 'Madhya Pradesh', 'Odisha']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Andhra Pradesh vs Madhya Pradesh vs Odisha', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Andhra Pradesh', 'Madhya Pradesh', 'Odisha']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Andhra Pradesh vs Madhya Pradesh vs Odisha', width=550, height=320)
     return chart


</code>
"
4030,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Jammu and Kashmir, Uttar Pradesh, and Punjab from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Jammu and Kashmir', 'Uttar Pradesh', 'Punjab']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Jammu and Kashmir vs Uttar Pradesh vs Punjab', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Jammu and Kashmir', 'Uttar Pradesh', 'Punjab']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Jammu and Kashmir vs Uttar Pradesh vs Punjab', width=550, height=320)
     return chart


</code>
"
4031,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Meghalaya, Chandigarh, and Chhattisgarh in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Meghalaya', 'Chandigarh', 'Chhattisgarh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Meghalaya, Chandigarh, UP – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Meghalaya', 'Chandigarh', 'Chhattisgarh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Meghalaya, Chandigarh, UP – 2023', width=550, height=320)
     return chart


</code>
"
4032,specific_pattern,Show a cumulative area chart of PM2.5 readings for Indore across 2022.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Indore') & (data['Timestamp'].dt.year == 2022)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Indore 2022', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Indore') & (data['Timestamp'].dt.year == 2022)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Indore 2022', width=600, height=300)
     return chart


</code>
"
4033,temporal_aggregation,Show the monthly average PM2.5 for Ankleshwar in 2019 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Ankleshwar') & (data['Timestamp'].dt.year == 2019)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Ankleshwar 2019', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Ankleshwar') & (data['Timestamp'].dt.year == 2019)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Ankleshwar 2019', width=450, height=280)


</code>
"
4034,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Tripura, Meghalaya, and Karnataka across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Tripura', 'Meghalaya', 'Karnataka']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Tripura', 'Meghalaya', 'Karnataka']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
4035,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Anantapur, Ramanathapuram, and Baran in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Anantapur', 'Ramanathapuram', 'Baran']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Anantapur vs Ramanathapuram vs Baran – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Anantapur', 'Ramanathapuram', 'Baran']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Anantapur vs Ramanathapuram vs Baran – 2024', width=550, height=320)
     return chart


</code>
"
4036,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Manipur, Puducherry, and Bihar in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Manipur', 'Puducherry', 'Bihar']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Manipur, Puducherry, UP – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Manipur', 'Puducherry', 'Bihar']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Manipur, Puducherry, UP – 2022', width=550, height=320)
     return chart


</code>
"
4037,specific_pattern,Show a cumulative area chart of PM2.5 readings for Sonipat across 2021.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Sonipat') & (data['Timestamp'].dt.year == 2021)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Sonipat 2021', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Sonipat') & (data['Timestamp'].dt.year == 2021)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Sonipat 2021', width=600, height=300)
     return chart


</code>
"
4038,specific_pattern,Show a cumulative area chart of PM2.5 readings for Saharsa across 2021.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Saharsa') & (data['Timestamp'].dt.year == 2021)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Saharsa 2021', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Saharsa') & (data['Timestamp'].dt.year == 2021)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Saharsa 2021', width=600, height=300)
     return chart


</code>
"
4039,temporal_aggregation,Show the monthly average PM10 trend for Yamuna Nagar from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Yamuna Nagar'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Yamuna Nagar (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Yamuna Nagar'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Yamuna Nagar (2019–2024)', width=600, height=300)
     return chart


</code>
"
4040,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Jammu and Kashmir, Uttarakhand, and Assam across 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Jammu and Kashmir', 'Uttarakhand', 'Assam']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Jammu and Kashmir', 'Uttarakhand', 'Assam']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
     return chart


</code>
"
4041,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Kolkata, Hubballi, and Meerut in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Kolkata', 'Hubballi', 'Meerut']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Kolkata vs Hubballi vs Meerut – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Kolkata', 'Hubballi', 'Meerut']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Kolkata vs Hubballi vs Meerut – 2023', width=550, height=320)
     return chart


</code>
"
4042,temporal_aggregation,Show the monthly average PM2.5 for Cuttack in 2023 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Cuttack') & (data['Timestamp'].dt.year == 2023)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Cuttack 2023', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Cuttack') & (data['Timestamp'].dt.year == 2023)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Cuttack 2023', width=450, height=280)


</code>
"
4043,temporal_aggregation,Show the monthly average PM2.5 for Mandi Gobindgarh in 2018 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Mandi Gobindgarh') & (data['Timestamp'].dt.year == 2018)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Mandi Gobindgarh 2018', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Mandi Gobindgarh') & (data['Timestamp'].dt.year == 2018)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Mandi Gobindgarh 2018', width=450, height=280)


</code>
"
4044,specific_pattern,Show a cumulative area chart of PM2.5 readings for Amaravati across 2021.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Amaravati') & (data['Timestamp'].dt.year == 2021)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Amaravati 2021', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Amaravati') & (data['Timestamp'].dt.year == 2021)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Amaravati 2021', width=600, height=300)
     return chart


</code>
"
4045,temporal_aggregation,Show a monthly bar chart of the number of days Uttar Pradesh exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2023.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Uttar Pradesh') & (data['Timestamp'].dt.year == 2023)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Uttar Pradesh Exceeded WHO PM2.5 Guideline per Month – 2023', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Uttar Pradesh') & (data['Timestamp'].dt.year == 2023)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Uttar Pradesh Exceeded WHO PM2.5 Guideline per Month – 2023', width=500, height=300)
     return chart


</code>
"
4046,temporal_aggregation,Show the monthly average PM2.5 for Tirupur in 2023 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Tirupur') & (data['Timestamp'].dt.year == 2023)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Tirupur 2023', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Tirupur') & (data['Timestamp'].dt.year == 2023)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Tirupur 2023', width=450, height=280)


</code>
"
4047,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Jammu and Kashmir, Sikkim, and Rajasthan from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Jammu and Kashmir', 'Sikkim', 'Rajasthan']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Jammu and Kashmir vs Sikkim vs Rajasthan', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Jammu and Kashmir', 'Sikkim', 'Rajasthan']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Jammu and Kashmir vs Sikkim vs Rajasthan', width=550, height=320)
     return chart


</code>
"
4048,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Meghalaya, Assam, and Rajasthan in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Meghalaya', 'Assam', 'Rajasthan']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Meghalaya, Assam, UP – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Meghalaya', 'Assam', 'Rajasthan']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Meghalaya, Assam, UP – 2022', width=550, height=320)
     return chart


</code>
"
4049,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Telangana, Tripura, and Gujarat from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Telangana', 'Tripura', 'Gujarat']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Telangana vs Tripura vs Gujarat', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Telangana', 'Tripura', 'Gujarat']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Telangana vs Tripura vs Gujarat', width=550, height=320)
     return chart


</code>
"
4050,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Gujarat, Sikkim, and Haryana across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Gujarat', 'Sikkim', 'Haryana']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Gujarat', 'Sikkim', 'Haryana']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
4051,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Nagaur, Bhopal, and Chhapra in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Nagaur', 'Bhopal', 'Chhapra']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Nagaur vs Bhopal vs Chhapra – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Nagaur', 'Bhopal', 'Chhapra']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Nagaur vs Bhopal vs Chhapra – 2023', width=550, height=320)
     return chart


</code>
"
4052,temporal_aggregation,Show the monthly average PM2.5 for Mandikhera in 2019 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Mandikhera') & (data['Timestamp'].dt.year == 2019)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Mandikhera 2019', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Mandikhera') & (data['Timestamp'].dt.year == 2019)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Mandikhera 2019', width=450, height=280)


</code>
"
4053,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Durgapur, Katni, and Nashik in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Durgapur', 'Katni', 'Nashik']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Durgapur vs Katni vs Nashik – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Durgapur', 'Katni', 'Nashik']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Durgapur vs Katni vs Nashik – 2023', width=550, height=320)
     return chart


</code>
"
4054,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Arunachal Pradesh, Andhra Pradesh, and Madhya Pradesh in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Arunachal Pradesh', 'Andhra Pradesh', 'Madhya Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Arunachal Pradesh, Andhra Pradesh, UP – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Arunachal Pradesh', 'Andhra Pradesh', 'Madhya Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Arunachal Pradesh, Andhra Pradesh, UP – 2024', width=550, height=320)
     return chart


</code>
"
4055,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Angul, Bettiah, and Gaya in 2019 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Angul', 'Bettiah', 'Gaya']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Angul vs Bettiah vs Gaya – 2019', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Angul', 'Bettiah', 'Gaya']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Angul vs Bettiah vs Gaya – 2019', width=550, height=320)
     return chart


</code>
"
4056,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Uttarakhand, Madhya Pradesh, and Himachal Pradesh in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Uttarakhand', 'Madhya Pradesh', 'Himachal Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Uttarakhand, Madhya Pradesh, UP – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Uttarakhand', 'Madhya Pradesh', 'Himachal Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Uttarakhand, Madhya Pradesh, UP – 2023', width=550, height=320)
     return chart


</code>
"
4057,temporal_aggregation,Show a monthly bar chart of the number of days Andhra Pradesh exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2020.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Andhra Pradesh') & (data['Timestamp'].dt.year == 2020)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Andhra Pradesh Exceeded WHO PM2.5 Guideline per Month – 2020', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Andhra Pradesh') & (data['Timestamp'].dt.year == 2020)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Andhra Pradesh Exceeded WHO PM2.5 Guideline per Month – 2020', width=500, height=300)
     return chart


</code>
"
4058,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Sikar, Fatehabad, and Ballabgarh in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Sikar', 'Fatehabad', 'Ballabgarh']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Sikar vs Fatehabad vs Ballabgarh – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Sikar', 'Fatehabad', 'Ballabgarh']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Sikar vs Fatehabad vs Ballabgarh – 2024', width=550, height=320)
     return chart


</code>
"
4059,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Chittorgarh, Kishanganj, and Vatva in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Chittorgarh', 'Kishanganj', 'Vatva']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Chittorgarh vs Kishanganj vs Vatva – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Chittorgarh', 'Kishanganj', 'Vatva']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Chittorgarh vs Kishanganj vs Vatva – 2023', width=550, height=320)
     return chart


</code>
"
4060,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Telangana, Chhattisgarh, and Chhattisgarh from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Telangana', 'Chhattisgarh', 'Chhattisgarh']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Telangana vs Chhattisgarh vs Chhattisgarh', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Telangana', 'Chhattisgarh', 'Chhattisgarh']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Telangana vs Chhattisgarh vs Chhattisgarh', width=550, height=320)
     return chart


</code>
"
4061,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Meghalaya, Tripura, and Uttarakhand in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Meghalaya', 'Tripura', 'Uttarakhand']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Meghalaya, Tripura, UP – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Meghalaya', 'Tripura', 'Uttarakhand']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Meghalaya, Tripura, UP – 2023', width=550, height=320)
     return chart


</code>
"
4062,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Punjab, Mizoram, and Jharkhand across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Punjab', 'Mizoram', 'Jharkhand']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Punjab', 'Mizoram', 'Jharkhand']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
4063,temporal_aggregation,Show a monthly bar chart of the number of days Gujarat exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2022.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Gujarat') & (data['Timestamp'].dt.year == 2022)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Gujarat Exceeded WHO PM2.5 Guideline per Month – 2022', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Gujarat') & (data['Timestamp'].dt.year == 2022)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Gujarat Exceeded WHO PM2.5 Guideline per Month – 2022', width=500, height=300)
     return chart


</code>
"
4064,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Rajasthan, Rajasthan, and Himachal Pradesh across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Rajasthan', 'Rajasthan', 'Himachal Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Rajasthan', 'Rajasthan', 'Himachal Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
4065,temporal_aggregation,Plot the weekly average PM2.5 for Kannur in 2021 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Kannur') & (data['Timestamp'].dt.year == 2021)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Kannur 2021', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Kannur') & (data['Timestamp'].dt.year == 2021)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Kannur 2021', width=600, height=300)
     return chart


</code>
"
4066,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Kerala, Tamil Nadu, and Rajasthan in 2019 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Kerala', 'Tamil Nadu', 'Rajasthan']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Kerala, Tamil Nadu, UP – 2019', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Kerala', 'Tamil Nadu', 'Rajasthan']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Kerala, Tamil Nadu, UP – 2019', width=550, height=320)
     return chart


</code>
"
4067,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Odisha stations in 2021, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Odisha') & (data['Timestamp'].dt.year == 2021)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Odisha Stations 2021', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Odisha') & (data['Timestamp'].dt.year == 2021)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Odisha Stations 2021', width=450, height=350)


</code>
"
4068,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Jammu and Kashmir, Jharkhand, and Karnataka from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Jammu and Kashmir', 'Jharkhand', 'Karnataka']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Jammu and Kashmir vs Jharkhand vs Karnataka', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Jammu and Kashmir', 'Jharkhand', 'Karnataka']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Jammu and Kashmir vs Jharkhand vs Karnataka', width=550, height=320)
     return chart


</code>
"
4069,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Tamil Nadu, Chhattisgarh, and Haryana in 2019 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Tamil Nadu', 'Chhattisgarh', 'Haryana']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Tamil Nadu, Chhattisgarh, UP – 2019', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Tamil Nadu', 'Chhattisgarh', 'Haryana']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Tamil Nadu, Chhattisgarh, UP – 2019', width=550, height=320)
     return chart


</code>
"
4070,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Madhya Pradesh, Sikkim, and Rajasthan in 2021 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Madhya Pradesh', 'Sikkim', 'Rajasthan']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Madhya Pradesh, Sikkim, UP – 2021', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Madhya Pradesh', 'Sikkim', 'Rajasthan']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Madhya Pradesh, Sikkim, UP – 2021', width=550, height=320)
     return chart


</code>
"
4071,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Uttar Pradesh, Delhi, and Chhattisgarh across 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Uttar Pradesh', 'Delhi', 'Chhattisgarh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Uttar Pradesh', 'Delhi', 'Chhattisgarh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
     return chart


</code>
"
4072,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Puducherry, Jammu and Kashmir, and Manipur from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Puducherry', 'Jammu and Kashmir', 'Manipur']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Puducherry vs Jammu and Kashmir vs Manipur', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Puducherry', 'Jammu and Kashmir', 'Manipur']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Puducherry vs Jammu and Kashmir vs Manipur', width=550, height=320)
     return chart


</code>
"
4073,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Bhilai, Mandikhera, and Bhiwandi in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Bhilai', 'Mandikhera', 'Bhiwandi']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Bhilai vs Mandikhera vs Bhiwandi – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Bhilai', 'Mandikhera', 'Bhiwandi']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Bhilai vs Mandikhera vs Bhiwandi – 2024', width=550, height=320)
     return chart


</code>
"
4074,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Himachal Pradesh stations in 2017, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Himachal Pradesh') & (data['Timestamp'].dt.year == 2017)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Himachal Pradesh Stations 2017', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Himachal Pradesh') & (data['Timestamp'].dt.year == 2017)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Himachal Pradesh Stations 2017', width=450, height=350)


</code>
"
4075,temporal_aggregation,Plot the weekly average PM2.5 for Panipat in 2019 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Panipat') & (data['Timestamp'].dt.year == 2019)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Panipat 2019', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Panipat') & (data['Timestamp'].dt.year == 2019)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Panipat 2019', width=600, height=300)
     return chart


</code>
"
4076,temporal_aggregation,Show the monthly average PM2.5 for Churu in 2020 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Churu') & (data['Timestamp'].dt.year == 2020)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Churu 2020', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Churu') & (data['Timestamp'].dt.year == 2020)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Churu 2020', width=450, height=280)


</code>
"
4077,temporal_aggregation,Show the monthly average PM2.5 for Hapur in 2023 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Hapur') & (data['Timestamp'].dt.year == 2023)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Hapur 2023', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Hapur') & (data['Timestamp'].dt.year == 2023)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Hapur 2023', width=450, height=280)


</code>
"
4078,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Rajasthan, Telangana, and Tamil Nadu in 2018 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Rajasthan', 'Telangana', 'Tamil Nadu']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2018)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Rajasthan, Telangana, UP – 2018', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Rajasthan', 'Telangana', 'Tamil Nadu']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2018)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Rajasthan, Telangana, UP – 2018', width=550, height=320)
     return chart


</code>
"
4079,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Puducherry, Andhra Pradesh, and Chhattisgarh across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Puducherry', 'Andhra Pradesh', 'Chhattisgarh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Puducherry', 'Andhra Pradesh', 'Chhattisgarh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
4080,temporal_aggregation,Show the monthly average PM2.5 for Haveri in 2017 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Haveri') & (data['Timestamp'].dt.year == 2017)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Haveri 2017', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Haveri') & (data['Timestamp'].dt.year == 2017)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Haveri 2017', width=450, height=280)


</code>
"
4081,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Jhalawar, Madurai, and Bulandshahr in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Jhalawar', 'Madurai', 'Bulandshahr']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Jhalawar vs Madurai vs Bulandshahr – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Jhalawar', 'Madurai', 'Bulandshahr']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Jhalawar vs Madurai vs Bulandshahr – 2022', width=550, height=320)
     return chart


</code>
"
4082,temporal_aggregation,Show the monthly average PM2.5 for Bulandshahr in 2019 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Bulandshahr') & (data['Timestamp'].dt.year == 2019)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Bulandshahr 2019', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Bulandshahr') & (data['Timestamp'].dt.year == 2019)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Bulandshahr 2019', width=450, height=280)


</code>
"
4083,temporal_aggregation,Show the monthly average PM10 trend for Gummidipoondi from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Gummidipoondi'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Gummidipoondi (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Gummidipoondi'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Gummidipoondi (2019–2024)', width=600, height=300)
     return chart


</code>
"
4084,temporal_aggregation,Show the monthly average PM2.5 for Bhilai in 2023 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Bhilai') & (data['Timestamp'].dt.year == 2023)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Bhilai 2023', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Bhilai') & (data['Timestamp'].dt.year == 2023)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Bhilai 2023', width=450, height=280)


</code>
"
4085,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Madhya Pradesh, Himachal Pradesh, and Madhya Pradesh across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Madhya Pradesh', 'Himachal Pradesh', 'Madhya Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Madhya Pradesh', 'Himachal Pradesh', 'Madhya Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
4086,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Puducherry, Haryana, and Chandigarh in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Puducherry', 'Haryana', 'Chandigarh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Puducherry, Haryana, UP – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Puducherry', 'Haryana', 'Chandigarh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Puducherry, Haryana, UP – 2023', width=550, height=320)
     return chart


</code>
"
4087,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Sikkim, Chandigarh, and Jammu and Kashmir across 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Sikkim', 'Chandigarh', 'Jammu and Kashmir']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Sikkim', 'Chandigarh', 'Jammu and Kashmir']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
     return chart


</code>
"
4088,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Andhra Pradesh, Mizoram, and Haryana in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Andhra Pradesh', 'Mizoram', 'Haryana']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Andhra Pradesh, Mizoram, UP – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Andhra Pradesh', 'Mizoram', 'Haryana']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Andhra Pradesh, Mizoram, UP – 2024', width=550, height=320)
     return chart


</code>
"
4089,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Srinagar, Begusarai, and Aizawl in 2020 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Srinagar', 'Begusarai', 'Aizawl']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2020)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Srinagar vs Begusarai vs Aizawl – 2020', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Srinagar', 'Begusarai', 'Aizawl']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2020)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Srinagar vs Begusarai vs Aizawl – 2020', width=550, height=320)
     return chart


</code>
"
4090,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Chandigarh, Odisha, and Sikkim across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Chandigarh', 'Odisha', 'Sikkim']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Chandigarh', 'Odisha', 'Sikkim']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
4091,temporal_aggregation,Show the monthly average PM2.5 for Charkhi Dadri in 2024 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Charkhi Dadri') & (data['Timestamp'].dt.year == 2024)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Charkhi Dadri 2024', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Charkhi Dadri') & (data['Timestamp'].dt.year == 2024)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Charkhi Dadri 2024', width=450, height=280)


</code>
"
4092,specific_pattern,Show a cumulative area chart of PM2.5 readings for Muzaffarnagar across 2019.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Muzaffarnagar') & (data['Timestamp'].dt.year == 2019)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Muzaffarnagar 2019', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Muzaffarnagar') & (data['Timestamp'].dt.year == 2019)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Muzaffarnagar 2019', width=600, height=300)
     return chart


</code>
"
4093,temporal_aggregation,Show the monthly average PM10 trend for Nagapattinam from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Nagapattinam'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Nagapattinam (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Nagapattinam'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Nagapattinam (2019–2024)', width=600, height=300)
     return chart


</code>
"
4094,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Tripura, Uttar Pradesh, and Rajasthan across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Tripura', 'Uttar Pradesh', 'Rajasthan']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Tripura', 'Uttar Pradesh', 'Rajasthan']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
4095,temporal_aggregation,Plot the weekly average PM2.5 for Ernakulam in 2020 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Ernakulam') & (data['Timestamp'].dt.year == 2020)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Ernakulam 2020', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Ernakulam') & (data['Timestamp'].dt.year == 2020)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Ernakulam 2020', width=600, height=300)
     return chart


</code>
"
4096,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Chamarajanagar, Ahmedabad, and Indore in 2018 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Chamarajanagar', 'Ahmedabad', 'Indore']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2018)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Chamarajanagar vs Ahmedabad vs Indore – 2018', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Chamarajanagar', 'Ahmedabad', 'Indore']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2018)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Chamarajanagar vs Ahmedabad vs Indore – 2018', width=550, height=320)
     return chart


</code>
"
4097,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Madhya Pradesh, Odisha, and Puducherry across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Madhya Pradesh', 'Odisha', 'Puducherry']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Madhya Pradesh', 'Odisha', 'Puducherry']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
4098,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Tamil Nadu, Andhra Pradesh, and Karnataka from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Tamil Nadu', 'Andhra Pradesh', 'Karnataka']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Tamil Nadu vs Andhra Pradesh vs Karnataka', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Tamil Nadu', 'Andhra Pradesh', 'Karnataka']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Tamil Nadu vs Andhra Pradesh vs Karnataka', width=550, height=320)
     return chart


</code>
"
4099,temporal_aggregation,Show the monthly average PM2.5 for Patiala in 2017 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Patiala') & (data['Timestamp'].dt.year == 2017)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Patiala 2017', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Patiala') & (data['Timestamp'].dt.year == 2017)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Patiala 2017', width=450, height=280)


</code>
"
4100,temporal_aggregation,Show the monthly average PM2.5 for Perundurai in 2019 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Perundurai') & (data['Timestamp'].dt.year == 2019)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Perundurai 2019', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Perundurai') & (data['Timestamp'].dt.year == 2019)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Perundurai 2019', width=450, height=280)


</code>
"
4101,specific_pattern,Plot the rolling 30-day average PM2.5 for Gujarat in 2023 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Gujarat') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Gujarat 2023', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Gujarat') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Gujarat 2023', width=600, height=300)


</code>
"
4102,specific_pattern,Show a cumulative area chart of PM2.5 readings for Damoh across 2024.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Damoh') & (data['Timestamp'].dt.year == 2024)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Damoh 2024', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Damoh') & (data['Timestamp'].dt.year == 2024)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Damoh 2024', width=600, height=300)
     return chart


</code>
"
4103,temporal_aggregation,Plot the weekly average PM2.5 for Nayagarh in 2023 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Nayagarh') & (data['Timestamp'].dt.year == 2023)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Nayagarh 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Nayagarh') & (data['Timestamp'].dt.year == 2023)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Nayagarh 2023', width=600, height=300)
     return chart


</code>
"
4104,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Gujarat, Odisha, and Gujarat across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Gujarat', 'Odisha', 'Gujarat']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Gujarat', 'Odisha', 'Gujarat']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
4105,spatio_temporal_aggregation,"Visualize the monthly average PM10 for West Bengal, Kerala, and Tripura in 2018 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['West Bengal', 'Kerala', 'Tripura']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2018)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: West Bengal, Kerala, UP – 2018', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['West Bengal', 'Kerala', 'Tripura']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2018)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: West Bengal, Kerala, UP – 2018', width=550, height=320)
     return chart


</code>
"
4106,temporal_aggregation,Plot the weekly average PM2.5 for Ghaziabad in 2019 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Ghaziabad') & (data['Timestamp'].dt.year == 2019)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Ghaziabad 2019', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Ghaziabad') & (data['Timestamp'].dt.year == 2019)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Ghaziabad 2019', width=600, height=300)
     return chart


</code>
"
4107,temporal_aggregation,Show the monthly average PM2.5 for Jhalawar in 2018 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Jhalawar') & (data['Timestamp'].dt.year == 2018)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Jhalawar 2018', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Jhalawar') & (data['Timestamp'].dt.year == 2018)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Jhalawar 2018', width=450, height=280)


</code>
"
4108,specific_pattern,Show a cumulative area chart of PM2.5 readings for Madikeri across 2021.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Madikeri') & (data['Timestamp'].dt.year == 2021)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Madikeri 2021', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Madikeri') & (data['Timestamp'].dt.year == 2021)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Madikeri 2021', width=600, height=300)
     return chart


</code>
"
4109,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Telangana stations in 2020, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Telangana') & (data['Timestamp'].dt.year == 2020)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Telangana Stations 2020', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Telangana') & (data['Timestamp'].dt.year == 2020)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Telangana Stations 2020', width=450, height=350)


</code>
"
4110,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Karnataka, Jharkhand, and Manipur in 2017 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Karnataka', 'Jharkhand', 'Manipur']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2017)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Karnataka, Jharkhand, UP – 2017', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Karnataka', 'Jharkhand', 'Manipur']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2017)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Karnataka, Jharkhand, UP – 2017', width=550, height=320)
     return chart


</code>
"
4111,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Chhattisgarh, Manipur, and Chhattisgarh in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Chhattisgarh', 'Manipur', 'Chhattisgarh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Chhattisgarh, Manipur, UP – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Chhattisgarh', 'Manipur', 'Chhattisgarh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Chhattisgarh, Manipur, UP – 2022', width=550, height=320)
     return chart


</code>
"
4112,temporal_aggregation,Show the monthly average PM10 trend for Chhapra from 2017 to 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Chhapra'].copy()
    df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Chhapra (2017–2022)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Chhapra'].copy()
     df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Chhapra (2017–2022)', width=600, height=300)
     return chart


</code>
"
4113,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Jind, Gadag, and Kishanganj in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Jind', 'Gadag', 'Kishanganj']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Jind vs Gadag vs Kishanganj – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Jind', 'Gadag', 'Kishanganj']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Jind vs Gadag vs Kishanganj – 2022', width=550, height=320)
     return chart


</code>
"
4114,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Tripura, Meghalaya, and Kerala in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Tripura', 'Meghalaya', 'Kerala']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Tripura, Meghalaya, UP – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Tripura', 'Meghalaya', 'Kerala']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Tripura, Meghalaya, UP – 2022', width=550, height=320)
     return chart


</code>
"
4115,temporal_aggregation,Plot the weekly average PM2.5 for Muzaffarnagar in 2021 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Muzaffarnagar') & (data['Timestamp'].dt.year == 2021)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Muzaffarnagar 2021', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Muzaffarnagar') & (data['Timestamp'].dt.year == 2021)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Muzaffarnagar 2021', width=600, height=300)
     return chart


</code>
"
4116,temporal_aggregation,Show the monthly average PM2.5 for Alwar in 2020 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Alwar') & (data['Timestamp'].dt.year == 2020)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Alwar 2020', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Alwar') & (data['Timestamp'].dt.year == 2020)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Alwar 2020', width=450, height=280)


</code>
"
4117,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Arunachal Pradesh, Punjab, and Arunachal Pradesh from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Arunachal Pradesh', 'Punjab', 'Arunachal Pradesh']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Arunachal Pradesh vs Punjab vs Arunachal Pradesh', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Arunachal Pradesh', 'Punjab', 'Arunachal Pradesh']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Arunachal Pradesh vs Punjab vs Arunachal Pradesh', width=550, height=320)
     return chart


</code>
"
4118,temporal_aggregation,Show the monthly average PM10 trend for Chittoor from 2017 to 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Chittoor'].copy()
    df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Chittoor (2017–2022)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Chittoor'].copy()
     df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Chittoor (2017–2022)', width=600, height=300)
     return chart


</code>
"
4119,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Tripura, Kerala, and Kerala across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Tripura', 'Kerala', 'Kerala']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Tripura', 'Kerala', 'Kerala']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
4120,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Madhya Pradesh, West Bengal, and Chhattisgarh from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Madhya Pradesh', 'West Bengal', 'Chhattisgarh']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Madhya Pradesh vs West Bengal vs Chhattisgarh', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Madhya Pradesh', 'West Bengal', 'Chhattisgarh']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Madhya Pradesh vs West Bengal vs Chhattisgarh', width=550, height=320)
     return chart


</code>
"
4121,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Munger, Nagpur, and Hisar in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Munger', 'Nagpur', 'Hisar']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Munger vs Nagpur vs Hisar – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Munger', 'Nagpur', 'Hisar']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Munger vs Nagpur vs Hisar – 2022', width=550, height=320)
     return chart


</code>
"
4122,temporal_aggregation,Show the monthly average PM2.5 for Panipat in 2022 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Panipat') & (data['Timestamp'].dt.year == 2022)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Panipat 2022', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Panipat') & (data['Timestamp'].dt.year == 2022)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Panipat 2022', width=450, height=280)


</code>
"
4123,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Rajasthan, Gujarat, and West Bengal across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Rajasthan', 'Gujarat', 'West Bengal']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Rajasthan', 'Gujarat', 'West Bengal']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
4124,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Arunachal Pradesh, Chhattisgarh, and Tripura across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Arunachal Pradesh', 'Chhattisgarh', 'Tripura']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Arunachal Pradesh', 'Chhattisgarh', 'Tripura']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
4125,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Chandigarh, Haryana, and Jammu and Kashmir across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Chandigarh', 'Haryana', 'Jammu and Kashmir']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Chandigarh', 'Haryana', 'Jammu and Kashmir']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
4126,specific_pattern,Plot the rolling 30-day average PM2.5 for Mizoram in 2017 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Mizoram') & (data['Timestamp'].dt.year == 2017)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Mizoram 2017', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Mizoram') & (data['Timestamp'].dt.year == 2017)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Mizoram 2017', width=600, height=300)


</code>
"
4127,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Odisha, Rajasthan, and Himachal Pradesh from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Odisha', 'Rajasthan', 'Himachal Pradesh']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Odisha vs Rajasthan vs Himachal Pradesh', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Odisha', 'Rajasthan', 'Himachal Pradesh']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Odisha vs Rajasthan vs Himachal Pradesh', width=550, height=320)
     return chart


</code>
"
4128,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Sikkim, Karnataka, and Mizoram in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Sikkim', 'Karnataka', 'Mizoram']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Sikkim, Karnataka, UP – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Sikkim', 'Karnataka', 'Mizoram']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Sikkim, Karnataka, UP – 2024', width=550, height=320)
     return chart


</code>
"
4129,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Telangana, Odisha, and Mizoram from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Telangana', 'Odisha', 'Mizoram']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Telangana vs Odisha vs Mizoram', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Telangana', 'Odisha', 'Mizoram']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Telangana vs Odisha vs Mizoram', width=550, height=320)
     return chart


</code>
"
4130,specific_pattern,Plot the rolling 30-day average PM2.5 for Madhya Pradesh in 2020 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Madhya Pradesh') & (data['Timestamp'].dt.year == 2020)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Madhya Pradesh 2020', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Madhya Pradesh') & (data['Timestamp'].dt.year == 2020)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Madhya Pradesh 2020', width=600, height=300)


</code>
"
4131,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Odisha, Telangana, and Karnataka across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Odisha', 'Telangana', 'Karnataka']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Odisha', 'Telangana', 'Karnataka']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
4132,temporal_aggregation,Show the monthly average PM2.5 for Tirupur in 2017 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Tirupur') & (data['Timestamp'].dt.year == 2017)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Tirupur 2017', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Tirupur') & (data['Timestamp'].dt.year == 2017)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Tirupur 2017', width=450, height=280)


</code>
"
4133,temporal_aggregation,Show the monthly average PM2.5 for Jaipur in 2020 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Jaipur') & (data['Timestamp'].dt.year == 2020)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Jaipur 2020', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Jaipur') & (data['Timestamp'].dt.year == 2020)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Jaipur 2020', width=450, height=280)


</code>
"
4134,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Gujarat, Tripura, and Telangana from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Gujarat', 'Tripura', 'Telangana']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Gujarat vs Tripura vs Telangana', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Gujarat', 'Tripura', 'Telangana']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Gujarat vs Tripura vs Telangana', width=550, height=320)
     return chart


</code>
"
4135,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Andhra Pradesh, Telangana, and Delhi in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Andhra Pradesh', 'Telangana', 'Delhi']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Andhra Pradesh, Telangana, UP – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Andhra Pradesh', 'Telangana', 'Delhi']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Andhra Pradesh, Telangana, UP – 2023', width=550, height=320)
     return chart


</code>
"
4136,temporal_aggregation,Show the monthly average PM2.5 for Karwar in 2020 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Karwar') & (data['Timestamp'].dt.year == 2020)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Karwar 2020', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Karwar') & (data['Timestamp'].dt.year == 2020)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Karwar 2020', width=450, height=280)


</code>
"
4137,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Koppal, Bhubaneswar, and Muzaffarnagar in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Koppal', 'Bhubaneswar', 'Muzaffarnagar']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Koppal vs Bhubaneswar vs Muzaffarnagar – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Koppal', 'Bhubaneswar', 'Muzaffarnagar']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Koppal vs Bhubaneswar vs Muzaffarnagar – 2023', width=550, height=320)
     return chart


</code>
"
4138,temporal_aggregation,Show the monthly average PM10 trend for Bahadurgarh from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Bahadurgarh'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Bahadurgarh (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Bahadurgarh'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Bahadurgarh (2019–2024)', width=600, height=300)
     return chart


</code>
"
4139,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Uttar Pradesh, Tamil Nadu, and Gujarat across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Uttar Pradesh', 'Tamil Nadu', 'Gujarat']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Uttar Pradesh', 'Tamil Nadu', 'Gujarat']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
4140,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Meghalaya, Jharkhand, and Tripura from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Meghalaya', 'Jharkhand', 'Tripura']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Meghalaya vs Jharkhand vs Tripura', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Meghalaya', 'Jharkhand', 'Tripura']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Meghalaya vs Jharkhand vs Tripura', width=550, height=320)
     return chart


</code>
"
4141,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Rajasthan, Maharashtra, and Kerala across 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Rajasthan', 'Maharashtra', 'Kerala']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Rajasthan', 'Maharashtra', 'Kerala']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
     return chart


</code>
"
4142,specific_pattern,Show a cumulative area chart of PM2.5 readings for Gurugram across 2018.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Gurugram') & (data['Timestamp'].dt.year == 2018)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Gurugram 2018', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Gurugram') & (data['Timestamp'].dt.year == 2018)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Gurugram 2018', width=600, height=300)
     return chart


</code>
"
4143,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Jharkhand, Nagaland, and Jharkhand in 2019 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Jharkhand', 'Nagaland', 'Jharkhand']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Jharkhand, Nagaland, UP – 2019', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Jharkhand', 'Nagaland', 'Jharkhand']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Jharkhand, Nagaland, UP – 2019', width=550, height=320)
     return chart


</code>
"
4144,temporal_aggregation,Show the monthly average PM10 trend for Kurukshetra  from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Kurukshetra '].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Kurukshetra  (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Kurukshetra '].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Kurukshetra  (2019–2024)', width=600, height=300)
     return chart


</code>
"
4145,specific_pattern,Show a cumulative area chart of PM2.5 readings for Naharlagun across 2022.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Naharlagun') & (data['Timestamp'].dt.year == 2022)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Naharlagun 2022', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Naharlagun') & (data['Timestamp'].dt.year == 2022)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Naharlagun 2022', width=600, height=300)
     return chart


</code>
"
4146,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Hanumangarh, Eloor, and Perundurai in 2020 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Hanumangarh', 'Eloor', 'Perundurai']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2020)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Hanumangarh vs Eloor vs Perundurai – 2020', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Hanumangarh', 'Eloor', 'Perundurai']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2020)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Hanumangarh vs Eloor vs Perundurai – 2020', width=550, height=320)
     return chart


</code>
"
4147,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Bihar stations in 2021, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Bihar') & (data['Timestamp'].dt.year == 2021)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Bihar Stations 2021', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Bihar') & (data['Timestamp'].dt.year == 2021)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Bihar Stations 2021', width=450, height=350)


</code>
"
4148,temporal_aggregation,Show the monthly average PM2.5 for Dausa in 2017 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Dausa') & (data['Timestamp'].dt.year == 2017)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Dausa 2017', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Dausa') & (data['Timestamp'].dt.year == 2017)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Dausa 2017', width=450, height=280)


</code>
"
4149,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Haryana, Mizoram, and Tripura in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Haryana', 'Mizoram', 'Tripura']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Haryana, Mizoram, UP – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Haryana', 'Mizoram', 'Tripura']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Haryana, Mizoram, UP – 2023', width=550, height=320)
     return chart


</code>
"
4150,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Delhi, Puducherry, and Jammu and Kashmir across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Delhi', 'Puducherry', 'Jammu and Kashmir']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Delhi', 'Puducherry', 'Jammu and Kashmir']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
4151,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Odisha, Assam, and Chandigarh across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Odisha', 'Assam', 'Chandigarh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Odisha', 'Assam', 'Chandigarh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
4152,temporal_aggregation,Show the monthly average PM2.5 for Muzaffarpur in 2018 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Muzaffarpur') & (data['Timestamp'].dt.year == 2018)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Muzaffarpur 2018', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Muzaffarpur') & (data['Timestamp'].dt.year == 2018)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Muzaffarpur 2018', width=450, height=280)


</code>
"
4153,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Odisha, Tripura, and Punjab in 2019 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Odisha', 'Tripura', 'Punjab']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Odisha, Tripura, UP – 2019', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Odisha', 'Tripura', 'Punjab']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Odisha, Tripura, UP – 2019', width=550, height=320)
     return chart


</code>
"
4154,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Tripura, Arunachal Pradesh, and Puducherry from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Tripura', 'Arunachal Pradesh', 'Puducherry']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Tripura vs Arunachal Pradesh vs Puducherry', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Tripura', 'Arunachal Pradesh', 'Puducherry']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Tripura vs Arunachal Pradesh vs Puducherry', width=550, height=320)
     return chart


</code>
"
4155,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for West Bengal, Arunachal Pradesh, and Bihar from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['West Bengal', 'Arunachal Pradesh', 'Bihar']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: West Bengal vs Arunachal Pradesh vs Bihar', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['West Bengal', 'Arunachal Pradesh', 'Bihar']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: West Bengal vs Arunachal Pradesh vs Bihar', width=550, height=320)
     return chart


</code>
"
4156,specific_pattern,Show a cumulative area chart of PM2.5 readings for Nashik across 2017.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Nashik') & (data['Timestamp'].dt.year == 2017)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Nashik 2017', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Nashik') & (data['Timestamp'].dt.year == 2017)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Nashik 2017', width=600, height=300)
     return chart


</code>
"
4157,temporal_aggregation,Show the monthly average PM10 trend for Karnal from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Karnal'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Karnal (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Karnal'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Karnal (2019–2024)', width=600, height=300)
     return chart


</code>
"
4158,temporal_aggregation,Plot the weekly average PM2.5 for Tirunelveli in 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Tirunelveli') & (data['Timestamp'].dt.year == 2024)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Tirunelveli 2024', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Tirunelveli') & (data['Timestamp'].dt.year == 2024)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Tirunelveli 2024', width=600, height=300)
     return chart


</code>
"
4159,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Andhra Pradesh, Puducherry, and Jharkhand in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Andhra Pradesh', 'Puducherry', 'Jharkhand']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Andhra Pradesh, Puducherry, UP – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Andhra Pradesh', 'Puducherry', 'Jharkhand']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Andhra Pradesh, Puducherry, UP – 2023', width=550, height=320)
     return chart


</code>
"
4160,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Bihar, Meghalaya, and Haryana across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Bihar', 'Meghalaya', 'Haryana']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Bihar', 'Meghalaya', 'Haryana']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
4161,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Puducherry, Sikkim, and Delhi from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Puducherry', 'Sikkim', 'Delhi']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Puducherry vs Sikkim vs Delhi', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Puducherry', 'Sikkim', 'Delhi']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Puducherry vs Sikkim vs Delhi', width=550, height=320)
     return chart


</code>
"
4162,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Yadgir, Dholpur, and Barrackpore in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Yadgir', 'Dholpur', 'Barrackpore']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Yadgir vs Dholpur vs Barrackpore – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Yadgir', 'Dholpur', 'Barrackpore']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Yadgir vs Dholpur vs Barrackpore – 2023', width=550, height=320)
     return chart


</code>
"
4163,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Jammu and Kashmir, Arunachal Pradesh, and West Bengal across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Jammu and Kashmir', 'Arunachal Pradesh', 'West Bengal']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Jammu and Kashmir', 'Arunachal Pradesh', 'West Bengal']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
4164,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Punjab, Haryana, and Chandigarh from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Punjab', 'Haryana', 'Chandigarh']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Punjab vs Haryana vs Chandigarh', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Punjab', 'Haryana', 'Chandigarh']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Punjab vs Haryana vs Chandigarh', width=550, height=320)
     return chart


</code>
"
4165,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Uttar Pradesh, Himachal Pradesh, and Nagaland from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Uttar Pradesh', 'Himachal Pradesh', 'Nagaland']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Uttar Pradesh vs Himachal Pradesh vs Nagaland', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Uttar Pradesh', 'Himachal Pradesh', 'Nagaland']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Uttar Pradesh vs Himachal Pradesh vs Nagaland', width=550, height=320)
     return chart


</code>
"
4166,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Telangana, Gujarat, and West Bengal in 2017 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Telangana', 'Gujarat', 'West Bengal']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2017)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Telangana, Gujarat, UP – 2017', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Telangana', 'Gujarat', 'West Bengal']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2017)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Telangana, Gujarat, UP – 2017', width=550, height=320)
     return chart


</code>
"
4167,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Manipur, Gujarat, and West Bengal in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Manipur', 'Gujarat', 'West Bengal']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Manipur, Gujarat, UP – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Manipur', 'Gujarat', 'West Bengal']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Manipur, Gujarat, UP – 2022', width=550, height=320)
     return chart


</code>
"
4168,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Vrindavan, Dausa, and Kalaburagi in 2018 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Vrindavan', 'Dausa', 'Kalaburagi']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2018)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Vrindavan vs Dausa vs Kalaburagi – 2018', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Vrindavan', 'Dausa', 'Kalaburagi']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2018)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Vrindavan vs Dausa vs Kalaburagi – 2018', width=550, height=320)
     return chart


</code>
"
4169,temporal_aggregation,Show the monthly average PM2.5 for Angul in 2018 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Angul') & (data['Timestamp'].dt.year == 2018)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Angul 2018', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Angul') & (data['Timestamp'].dt.year == 2018)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Angul 2018', width=450, height=280)


</code>
"
4170,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Mizoram, Chandigarh, and Odisha in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Mizoram', 'Chandigarh', 'Odisha']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Mizoram, Chandigarh, UP – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Mizoram', 'Chandigarh', 'Odisha']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Mizoram, Chandigarh, UP – 2024', width=550, height=320)
     return chart


</code>
"
4171,temporal_aggregation,Plot the weekly average PM2.5 for Singrauli in 2020 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Singrauli') & (data['Timestamp'].dt.year == 2020)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Singrauli 2020', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Singrauli') & (data['Timestamp'].dt.year == 2020)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Singrauli 2020', width=600, height=300)
     return chart


</code>
"
4172,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Gorakhpur, Araria, and Ajmer in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Gorakhpur', 'Araria', 'Ajmer']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Gorakhpur vs Araria vs Ajmer – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Gorakhpur', 'Araria', 'Ajmer']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Gorakhpur vs Araria vs Ajmer – 2024', width=550, height=320)
     return chart


</code>
"
4173,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Suakati, Tirupur, and Noida in 2019 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Suakati', 'Tirupur', 'Noida']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Suakati vs Tirupur vs Noida – 2019', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Suakati', 'Tirupur', 'Noida']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Suakati vs Tirupur vs Noida – 2019', width=550, height=320)
     return chart


</code>
"
4174,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Karnataka, Arunachal Pradesh, and Rajasthan from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Karnataka', 'Arunachal Pradesh', 'Rajasthan']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Karnataka vs Arunachal Pradesh vs Rajasthan', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Karnataka', 'Arunachal Pradesh', 'Rajasthan']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Karnataka vs Arunachal Pradesh vs Rajasthan', width=550, height=320)
     return chart


</code>
"
4175,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Manipur, Chandigarh, and Odisha across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Manipur', 'Chandigarh', 'Odisha']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Manipur', 'Chandigarh', 'Odisha']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
4176,temporal_aggregation,Show the monthly average PM10 trend for Ahmedabad from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Ahmedabad'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Ahmedabad (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Ahmedabad'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Ahmedabad (2019–2024)', width=600, height=300)
     return chart


</code>
"
4177,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Bihar, Jharkhand, and Karnataka in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Bihar', 'Jharkhand', 'Karnataka']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Bihar, Jharkhand, UP – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Bihar', 'Jharkhand', 'Karnataka']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Bihar, Jharkhand, UP – 2024', width=550, height=320)
     return chart


</code>
"
4178,temporal_aggregation,Show the monthly average PM10 trend for Pimpri-Chinchwad from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Pimpri-Chinchwad'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Pimpri-Chinchwad (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Pimpri-Chinchwad'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Pimpri-Chinchwad (2019–2024)', width=600, height=300)
     return chart


</code>
"
4179,temporal_aggregation,Plot the weekly average PM2.5 for Bihar Sharif in 2023 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Bihar Sharif') & (data['Timestamp'].dt.year == 2023)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Bihar Sharif 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Bihar Sharif') & (data['Timestamp'].dt.year == 2023)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Bihar Sharif 2023', width=600, height=300)
     return chart


</code>
"
4180,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Manipur, Manipur, and Manipur across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Manipur', 'Manipur', 'Manipur']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Manipur', 'Manipur', 'Manipur']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
4181,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Jalore, Bathinda, and Vijayawada in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Jalore', 'Bathinda', 'Vijayawada']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Jalore vs Bathinda vs Vijayawada – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Jalore', 'Bathinda', 'Vijayawada']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Jalore vs Bathinda vs Vijayawada – 2024', width=550, height=320)
     return chart


</code>
"
4182,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Chandigarh, Jharkhand, and Rajasthan in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Chandigarh', 'Jharkhand', 'Rajasthan']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Chandigarh, Jharkhand, UP – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Chandigarh', 'Jharkhand', 'Rajasthan']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Chandigarh, Jharkhand, UP – 2022', width=550, height=320)
     return chart


</code>
"
4183,specific_pattern,Show a cumulative area chart of PM2.5 readings for Pali across 2024.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Pali') & (data['Timestamp'].dt.year == 2024)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Pali 2024', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Pali') & (data['Timestamp'].dt.year == 2024)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Pali 2024', width=600, height=300)
     return chart


</code>
"
4184,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Rohtak, Kolhapur, and Sirohi in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Rohtak', 'Kolhapur', 'Sirohi']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Rohtak vs Kolhapur vs Sirohi – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Rohtak', 'Kolhapur', 'Sirohi']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Rohtak vs Kolhapur vs Sirohi – 2022', width=550, height=320)
     return chart


</code>
"
4185,specific_pattern,Plot the rolling 30-day average PM2.5 for Odisha in 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Odisha') & (data['Timestamp'].dt.year == 2024)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Odisha 2024', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Odisha') & (data['Timestamp'].dt.year == 2024)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Odisha 2024', width=600, height=300)


</code>
"
4186,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Nagaland, Bihar, and Karnataka from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Nagaland', 'Bihar', 'Karnataka']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Nagaland vs Bihar vs Karnataka', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Nagaland', 'Bihar', 'Karnataka']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Nagaland vs Bihar vs Karnataka', width=550, height=320)
     return chart


</code>
"
4187,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Gujarat, Himachal Pradesh, and Gujarat from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Gujarat', 'Himachal Pradesh', 'Gujarat']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Gujarat vs Himachal Pradesh vs Gujarat', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Gujarat', 'Himachal Pradesh', 'Gujarat']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Gujarat vs Himachal Pradesh vs Gujarat', width=550, height=320)
     return chart


</code>
"
4188,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Virar, Rajamahendravaram, and Madurai in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Virar', 'Rajamahendravaram', 'Madurai']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Virar vs Rajamahendravaram vs Madurai – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Virar', 'Rajamahendravaram', 'Madurai']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Virar vs Rajamahendravaram vs Madurai – 2022', width=550, height=320)
     return chart


</code>
"
4189,specific_pattern,Show a cumulative area chart of PM2.5 readings for Visakhapatnam across 2019.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Visakhapatnam') & (data['Timestamp'].dt.year == 2019)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Visakhapatnam 2019', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Visakhapatnam') & (data['Timestamp'].dt.year == 2019)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Visakhapatnam 2019', width=600, height=300)
     return chart


</code>
"
4190,temporal_aggregation,Show the monthly average PM2.5 for Howrah in 2020 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Howrah') & (data['Timestamp'].dt.year == 2020)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Howrah 2020', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Howrah') & (data['Timestamp'].dt.year == 2020)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Howrah 2020', width=450, height=280)


</code>
"
4191,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Kanchipuram, Bhubaneswar, and Vatva in 2019 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Kanchipuram', 'Bhubaneswar', 'Vatva']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Kanchipuram vs Bhubaneswar vs Vatva – 2019', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Kanchipuram', 'Bhubaneswar', 'Vatva']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Kanchipuram vs Bhubaneswar vs Vatva – 2019', width=550, height=320)
     return chart


</code>
"
4192,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Bihar, Tripura, and Arunachal Pradesh in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Bihar', 'Tripura', 'Arunachal Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Bihar, Tripura, UP – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Bihar', 'Tripura', 'Arunachal Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Bihar, Tripura, UP – 2024', width=550, height=320)
     return chart


</code>
"
4193,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Sikkim, Madhya Pradesh, and Arunachal Pradesh across 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Sikkim', 'Madhya Pradesh', 'Arunachal Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Sikkim', 'Madhya Pradesh', 'Arunachal Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
     return chart


</code>
"
4194,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Nagaur, Cuttack, and Siliguri in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Nagaur', 'Cuttack', 'Siliguri']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Nagaur vs Cuttack vs Siliguri – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Nagaur', 'Cuttack', 'Siliguri']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Nagaur vs Cuttack vs Siliguri – 2022', width=550, height=320)
     return chart


</code>
"
4195,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Telangana stations in 2023, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Telangana') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Telangana Stations 2023', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Telangana') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Telangana Stations 2023', width=450, height=350)


</code>
"
4196,specific_pattern,Show a cumulative area chart of PM2.5 readings for Jalandhar across 2023.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Jalandhar') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Jalandhar 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Jalandhar') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Jalandhar 2023', width=600, height=300)
     return chart


</code>
"
4197,temporal_aggregation,Show the monthly average PM2.5 for Churu in 2022 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Churu') & (data['Timestamp'].dt.year == 2022)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Churu 2022', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Churu') & (data['Timestamp'].dt.year == 2022)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Churu 2022', width=450, height=280)


</code>
"
4198,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Madhya Pradesh, Meghalaya, and Bihar across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Madhya Pradesh', 'Meghalaya', 'Bihar']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Madhya Pradesh', 'Meghalaya', 'Bihar']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
4199,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Punjab, Haryana, and Andhra Pradesh from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Punjab', 'Haryana', 'Andhra Pradesh']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Punjab vs Haryana vs Andhra Pradesh', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Punjab', 'Haryana', 'Andhra Pradesh']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Punjab vs Haryana vs Andhra Pradesh', width=550, height=320)
     return chart


</code>
"
4200,temporal_aggregation,Show the monthly average PM2.5 for Parbhani in 2017 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Parbhani') & (data['Timestamp'].dt.year == 2017)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Parbhani 2017', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Parbhani') & (data['Timestamp'].dt.year == 2017)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Parbhani 2017', width=450, height=280)


</code>
"
4201,specific_pattern,Plot the rolling 30-day average PM2.5 for Tripura in 2020 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Tripura') & (data['Timestamp'].dt.year == 2020)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Tripura 2020', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Tripura') & (data['Timestamp'].dt.year == 2020)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Tripura 2020', width=600, height=300)


</code>
"
4202,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Rajasthan, Jammu and Kashmir, and West Bengal across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Rajasthan', 'Jammu and Kashmir', 'West Bengal']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Rajasthan', 'Jammu and Kashmir', 'West Bengal']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
4203,spatial_aggregation,Plot the top 9 states by average PM2.5 in 2021 as a horizontal bar chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['Timestamp'].dt.year == 2021]
    df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
    df = df.nlargest(9, 'PM2.5')
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('state:N', sort='-x', title='State'),
        color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
        tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Top 9 States by Average PM2.5 in 2021', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['Timestamp'].dt.year == 2021]
     df = df.groupby('state')['PM2.5'].mean().reset_index().dropna()
     df = df.nlargest(9, 'PM2.5')
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('state:N', sort='-x', title='State'),
         color=alt.Color('PM2\.5:Q', scale=alt.Scale(scheme='reds'), legend=None),
         tooltip=['state:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Top 9 States by Average PM2.5 in 2021', width=500, height=300)
     return chart


</code>
"
4204,specific_pattern,Show a cumulative area chart of PM2.5 readings for Davanagere across 2021.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Davanagere') & (data['Timestamp'].dt.year == 2021)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Davanagere 2021', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Davanagere') & (data['Timestamp'].dt.year == 2021)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Davanagere 2021', width=600, height=300)
     return chart


</code>
"
4205,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Mizoram, Jammu and Kashmir, and Assam from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Mizoram', 'Jammu and Kashmir', 'Assam']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Mizoram vs Jammu and Kashmir vs Assam', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Mizoram', 'Jammu and Kashmir', 'Assam']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Mizoram vs Jammu and Kashmir vs Assam', width=550, height=320)
     return chart


</code>
"
4206,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Punjab, Andhra Pradesh, and Nagaland in 2019 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Punjab', 'Andhra Pradesh', 'Nagaland']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Punjab, Andhra Pradesh, UP – 2019', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Punjab', 'Andhra Pradesh', 'Nagaland']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Punjab, Andhra Pradesh, UP – 2019', width=550, height=320)
     return chart


</code>
"
4207,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Madhya Pradesh, Karnataka, and Kerala across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Madhya Pradesh', 'Karnataka', 'Kerala']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Madhya Pradesh', 'Karnataka', 'Kerala']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
4208,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Haryana stations in 2021, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Haryana') & (data['Timestamp'].dt.year == 2021)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Haryana Stations 2021', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Haryana') & (data['Timestamp'].dt.year == 2021)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Haryana Stations 2021', width=450, height=350)


</code>
"
4209,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Jammu and Kashmir, Punjab, and Gujarat from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Jammu and Kashmir', 'Punjab', 'Gujarat']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Jammu and Kashmir vs Punjab vs Gujarat', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Jammu and Kashmir', 'Punjab', 'Gujarat']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Jammu and Kashmir vs Punjab vs Gujarat', width=550, height=320)
     return chart


</code>
"
4210,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Madhya Pradesh, Puducherry, and Meghalaya across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Madhya Pradesh', 'Puducherry', 'Meghalaya']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Madhya Pradesh', 'Puducherry', 'Meghalaya']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
4211,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Chhattisgarh, Gujarat, and Bihar from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Chhattisgarh', 'Gujarat', 'Bihar']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Chhattisgarh vs Gujarat vs Bihar', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Chhattisgarh', 'Gujarat', 'Bihar']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Chhattisgarh vs Gujarat vs Bihar', width=550, height=320)
     return chart


</code>
"
4212,temporal_aggregation,Show the monthly average PM2.5 for Varanasi in 2019 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Varanasi') & (data['Timestamp'].dt.year == 2019)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Varanasi 2019', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Varanasi') & (data['Timestamp'].dt.year == 2019)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Varanasi 2019', width=450, height=280)


</code>
"
4213,temporal_aggregation,Show the monthly average PM2.5 for Thrissur in 2020 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Thrissur') & (data['Timestamp'].dt.year == 2020)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Thrissur 2020', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Thrissur') & (data['Timestamp'].dt.year == 2020)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Thrissur 2020', width=450, height=280)


</code>
"
4214,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Bathinda, Hapur, and Maihar in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Bathinda', 'Hapur', 'Maihar']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Bathinda vs Hapur vs Maihar – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Bathinda', 'Hapur', 'Maihar']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Bathinda vs Hapur vs Maihar – 2022', width=550, height=320)
     return chart


</code>
"
4215,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Bihar, West Bengal, and Delhi across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Bihar', 'West Bengal', 'Delhi']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Bihar', 'West Bengal', 'Delhi']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
4216,specific_pattern,Show a cumulative area chart of PM2.5 readings for Pune across 2024.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Pune') & (data['Timestamp'].dt.year == 2024)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Pune 2024', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Pune') & (data['Timestamp'].dt.year == 2024)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Pune 2024', width=600, height=300)
     return chart


</code>
"
4217,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Tamil Nadu, Sikkim, and West Bengal in 2021 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Tamil Nadu', 'Sikkim', 'West Bengal']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Tamil Nadu, Sikkim, UP – 2021', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Tamil Nadu', 'Sikkim', 'West Bengal']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Tamil Nadu, Sikkim, UP – 2021', width=550, height=320)
     return chart


</code>
"
4218,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Jammu and Kashmir, Odisha, and West Bengal from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Jammu and Kashmir', 'Odisha', 'West Bengal']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Jammu and Kashmir vs Odisha vs West Bengal', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Jammu and Kashmir', 'Odisha', 'West Bengal']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Jammu and Kashmir vs Odisha vs West Bengal', width=550, height=320)
     return chart


</code>
"
4219,temporal_aggregation,Show the monthly average PM10 trend for Delhi from 2017 to 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Delhi'].copy()
    df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Delhi (2017–2022)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Delhi'].copy()
     df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Delhi (2017–2022)', width=600, height=300)
     return chart


</code>
"
4220,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Telangana, Sikkim, and Kerala across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Telangana', 'Sikkim', 'Kerala']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Telangana', 'Sikkim', 'Kerala']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
4221,temporal_aggregation,Show the monthly average PM2.5 for Dehradun in 2020 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Dehradun') & (data['Timestamp'].dt.year == 2020)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Dehradun 2020', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Dehradun') & (data['Timestamp'].dt.year == 2020)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Dehradun 2020', width=450, height=280)


</code>
"
4222,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Dhule, Bengaluru, and Kurukshetra  in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Dhule', 'Bengaluru', 'Kurukshetra ']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Dhule vs Bengaluru vs Kurukshetra  – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Dhule', 'Bengaluru', 'Kurukshetra ']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Dhule vs Bengaluru vs Kurukshetra  – 2022', width=550, height=320)
     return chart


</code>
"
4223,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Perundurai, Nagaon, and Purnia in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Perundurai', 'Nagaon', 'Purnia']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Perundurai vs Nagaon vs Purnia – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Perundurai', 'Nagaon', 'Purnia']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Perundurai vs Nagaon vs Purnia – 2023', width=550, height=320)
     return chart


</code>
"
4224,specific_pattern,Plot the rolling 30-day average PM2.5 for Maharashtra in 2019 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Maharashtra') & (data['Timestamp'].dt.year == 2019)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Maharashtra 2019', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Maharashtra') & (data['Timestamp'].dt.year == 2019)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Maharashtra 2019', width=600, height=300)


</code>
"
4225,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Puducherry, Mizoram, and Sikkim in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Puducherry', 'Mizoram', 'Sikkim']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Puducherry, Mizoram, UP – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Puducherry', 'Mizoram', 'Sikkim']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Puducherry, Mizoram, UP – 2022', width=550, height=320)
     return chart


</code>
"
4226,temporal_aggregation,Plot the weekly average PM2.5 for Muzaffarnagar in 2023 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Muzaffarnagar') & (data['Timestamp'].dt.year == 2023)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Muzaffarnagar 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Muzaffarnagar') & (data['Timestamp'].dt.year == 2023)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Muzaffarnagar 2023', width=600, height=300)
     return chart


</code>
"
4227,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Jharkhand, Odisha, and Haryana from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Jharkhand', 'Odisha', 'Haryana']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Jharkhand vs Odisha vs Haryana', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Jharkhand', 'Odisha', 'Haryana']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Jharkhand vs Odisha vs Haryana', width=550, height=320)
     return chart


</code>
"
4228,specific_pattern,Plot the rolling 30-day average PM2.5 for Maharashtra in 2023 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Maharashtra') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Maharashtra 2023', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Maharashtra') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Maharashtra 2023', width=600, height=300)


</code>
"
4229,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Gujarat, Meghalaya, and Assam in 2021 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Gujarat', 'Meghalaya', 'Assam']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Gujarat, Meghalaya, UP – 2021', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Gujarat', 'Meghalaya', 'Assam']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Gujarat, Meghalaya, UP – 2021', width=550, height=320)
     return chart


</code>
"
4230,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Uttar Pradesh, Bihar, and Haryana from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Uttar Pradesh', 'Bihar', 'Haryana']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Uttar Pradesh vs Bihar vs Haryana', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Uttar Pradesh', 'Bihar', 'Haryana']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Uttar Pradesh vs Bihar vs Haryana', width=550, height=320)
     return chart


</code>
"
4231,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Sikkim, Puducherry, and Jharkhand from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Sikkim', 'Puducherry', 'Jharkhand']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Sikkim vs Puducherry vs Jharkhand', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Sikkim', 'Puducherry', 'Jharkhand']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Sikkim vs Puducherry vs Jharkhand', width=550, height=320)
     return chart


</code>
"
4232,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Andhra Pradesh, Assam, and Rajasthan in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Andhra Pradesh', 'Assam', 'Rajasthan']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Andhra Pradesh, Assam, UP – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Andhra Pradesh', 'Assam', 'Rajasthan']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Andhra Pradesh, Assam, UP – 2022', width=550, height=320)
     return chart


</code>
"
4233,temporal_aggregation,Plot the weekly average PM2.5 for Jaipur in 2018 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Jaipur') & (data['Timestamp'].dt.year == 2018)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Jaipur 2018', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Jaipur') & (data['Timestamp'].dt.year == 2018)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Jaipur 2018', width=600, height=300)
     return chart


</code>
"
4234,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for West Bengal, Tripura, and Madhya Pradesh across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['West Bengal', 'Tripura', 'Madhya Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['West Bengal', 'Tripura', 'Madhya Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
4235,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for West Bengal, Haryana, and Rajasthan across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['West Bengal', 'Haryana', 'Rajasthan']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['West Bengal', 'Haryana', 'Rajasthan']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
4236,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Tripura, Madhya Pradesh, and Meghalaya in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Tripura', 'Madhya Pradesh', 'Meghalaya']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Tripura, Madhya Pradesh, UP – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Tripura', 'Madhya Pradesh', 'Meghalaya']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Tripura, Madhya Pradesh, UP – 2023', width=550, height=320)
     return chart


</code>
"
4237,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Delhi, Uttarakhand, and Tripura from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Delhi', 'Uttarakhand', 'Tripura']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Delhi vs Uttarakhand vs Tripura', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Delhi', 'Uttarakhand', 'Tripura']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Delhi vs Uttarakhand vs Tripura', width=550, height=320)
     return chart


</code>
"
4238,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Bahadurgarh, Mandi Gobindgarh, and Ramanagara in 2019 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Bahadurgarh', 'Mandi Gobindgarh', 'Ramanagara']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Bahadurgarh vs Mandi Gobindgarh vs Ramanagara – 2019', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Bahadurgarh', 'Mandi Gobindgarh', 'Ramanagara']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Bahadurgarh vs Mandi Gobindgarh vs Ramanagara – 2019', width=550, height=320)
     return chart


</code>
"
4239,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Punjab, Delhi, and Karnataka from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Punjab', 'Delhi', 'Karnataka']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Punjab vs Delhi vs Karnataka', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Punjab', 'Delhi', 'Karnataka']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Punjab vs Delhi vs Karnataka', width=550, height=320)
     return chart


</code>
"
4240,specific_pattern,Show a cumulative area chart of PM2.5 readings for Visakhapatnam across 2023.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Visakhapatnam') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Visakhapatnam 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Visakhapatnam') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Visakhapatnam 2023', width=600, height=300)
     return chart


</code>
"
4241,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Manipur, Puducherry, and Tamil Nadu in 2019 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Manipur', 'Puducherry', 'Tamil Nadu']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Manipur, Puducherry, UP – 2019', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Manipur', 'Puducherry', 'Tamil Nadu']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Manipur, Puducherry, UP – 2019', width=550, height=320)
     return chart


</code>
"
4242,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Chhattisgarh, Mizoram, and Manipur in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Chhattisgarh', 'Mizoram', 'Manipur']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Chhattisgarh, Mizoram, UP – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Chhattisgarh', 'Mizoram', 'Manipur']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Chhattisgarh, Mizoram, UP – 2023', width=550, height=320)
     return chart


</code>
"
4243,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Puducherry stations in 2023, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Puducherry') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Puducherry Stations 2023', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Puducherry') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Puducherry Stations 2023', width=450, height=350)


</code>
"
4244,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Punjab, Rajasthan, and Kerala across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Punjab', 'Rajasthan', 'Kerala']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Punjab', 'Rajasthan', 'Kerala']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
4245,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Tripura, Jammu and Kashmir, and Mizoram from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Tripura', 'Jammu and Kashmir', 'Mizoram']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Tripura vs Jammu and Kashmir vs Mizoram', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Tripura', 'Jammu and Kashmir', 'Mizoram']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Tripura vs Jammu and Kashmir vs Mizoram', width=550, height=320)
     return chart


</code>
"
4246,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for West Bengal, Jammu and Kashmir, and Odisha from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['West Bengal', 'Jammu and Kashmir', 'Odisha']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: West Bengal vs Jammu and Kashmir vs Odisha', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['West Bengal', 'Jammu and Kashmir', 'Odisha']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: West Bengal vs Jammu and Kashmir vs Odisha', width=550, height=320)
     return chart


</code>
"
4247,temporal_aggregation,Show the monthly average PM10 trend for Bagalkot from 2017 to 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Bagalkot'].copy()
    df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Bagalkot (2017–2022)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Bagalkot'].copy()
     df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Bagalkot (2017–2022)', width=600, height=300)
     return chart


</code>
"
4248,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Pali, Yamuna Nagar, and Badlapur in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Pali', 'Yamuna Nagar', 'Badlapur']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Pali vs Yamuna Nagar vs Badlapur – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Pali', 'Yamuna Nagar', 'Badlapur']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Pali vs Yamuna Nagar vs Badlapur – 2024', width=550, height=320)
     return chart


</code>
"
4249,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Punjab, Chandigarh, and Puducherry across 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Punjab', 'Chandigarh', 'Puducherry']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Punjab', 'Chandigarh', 'Puducherry']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
     return chart


</code>
"
4250,temporal_aggregation,Plot the weekly average PM2.5 for Jind in 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Jind') & (data['Timestamp'].dt.year == 2024)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Jind 2024', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Jind') & (data['Timestamp'].dt.year == 2024)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Jind 2024', width=600, height=300)
     return chart


</code>
"
4251,temporal_aggregation,Plot the weekly average PM2.5 for Indore in 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Indore') & (data['Timestamp'].dt.year == 2024)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Indore 2024', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Indore') & (data['Timestamp'].dt.year == 2024)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Indore 2024', width=600, height=300)
     return chart


</code>
"
4252,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Jharkhand, Madhya Pradesh, and Puducherry across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Jharkhand', 'Madhya Pradesh', 'Puducherry']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Jharkhand', 'Madhya Pradesh', 'Puducherry']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
4253,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Maihar, Nashik, and Chennai in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Maihar', 'Nashik', 'Chennai']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Maihar vs Nashik vs Chennai – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Maihar', 'Nashik', 'Chennai']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Maihar vs Nashik vs Chennai – 2023', width=550, height=320)
     return chart


</code>
"
4254,temporal_aggregation,Plot the weekly average PM2.5 for Begusarai in 2023 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Begusarai') & (data['Timestamp'].dt.year == 2023)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Begusarai 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Begusarai') & (data['Timestamp'].dt.year == 2023)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Begusarai 2023', width=600, height=300)
     return chart


</code>
"
4255,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Bengaluru, Shillong, and Sangli in 2017 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Bengaluru', 'Shillong', 'Sangli']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2017)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Bengaluru vs Shillong vs Sangli – 2017', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Bengaluru', 'Shillong', 'Sangli']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2017)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Bengaluru vs Shillong vs Sangli – 2017', width=550, height=320)
     return chart


</code>
"
4256,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for West Bengal stations in 2017, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'West Bengal') & (data['Timestamp'].dt.year == 2017)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – West Bengal Stations 2017', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'West Bengal') & (data['Timestamp'].dt.year == 2017)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – West Bengal Stations 2017', width=450, height=350)


</code>
"
4257,spatial_aggregation,"Scatter plot PM2.5 vs PM10 for Sikkim stations in 2021, with a regression trend line.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Sikkim') & (data['Timestamp'].dt.year == 2021)]
    df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
    scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
        x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
    )
    trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
    return (scatter + trend).properties(title='PM2.5 vs PM10 – Sikkim Stations 2021', width=450, height=350)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Sikkim') & (data['Timestamp'].dt.year == 2021)]
     df = df.groupby('station')[['PM2.5','PM10']].mean().reset_index().dropna()
     scatter = alt.Chart(df).mark_point(filled=True, size=80, color='steelblue').encode(
         x=alt.X('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=['station:N', alt.Tooltip('PM2\.5:Q', format='.1f'), alt.Tooltip('PM10:Q', format='.1f')]
     )
     trend = scatter.transform_regression('PM2\.5','PM10').mark_line(color='firebrick', strokeDash=[5,3])
     return (scatter + trend).properties(title='PM2.5 vs PM10 – Sikkim Stations 2021', width=450, height=350)


</code>
"
4258,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Jharkhand, Kerala, and Karnataka from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Jharkhand', 'Kerala', 'Karnataka']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Jharkhand vs Kerala vs Karnataka', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Jharkhand', 'Kerala', 'Karnataka']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Jharkhand vs Kerala vs Karnataka', width=550, height=320)
     return chart


</code>
"
4259,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Arunachal Pradesh, Rajasthan, and Delhi in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Arunachal Pradesh', 'Rajasthan', 'Delhi']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Arunachal Pradesh, Rajasthan, UP – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Arunachal Pradesh', 'Rajasthan', 'Delhi']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Arunachal Pradesh, Rajasthan, UP – 2024', width=550, height=320)
     return chart


</code>
"
4260,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Odisha, Himachal Pradesh, and Nagaland from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Odisha', 'Himachal Pradesh', 'Nagaland']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Odisha vs Himachal Pradesh vs Nagaland', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Odisha', 'Himachal Pradesh', 'Nagaland']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Odisha vs Himachal Pradesh vs Nagaland', width=550, height=320)
     return chart


</code>
"
4261,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Jabalpur, Panchkula, and Gummidipoondi in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Jabalpur', 'Panchkula', 'Gummidipoondi']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Jabalpur vs Panchkula vs Gummidipoondi – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Jabalpur', 'Panchkula', 'Gummidipoondi']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Jabalpur vs Panchkula vs Gummidipoondi – 2022', width=550, height=320)
     return chart


</code>
"
4262,temporal_aggregation,Show the monthly average PM2.5 for Sawai Madhopur in 2024 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Sawai Madhopur') & (data['Timestamp'].dt.year == 2024)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Sawai Madhopur 2024', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Sawai Madhopur') & (data['Timestamp'].dt.year == 2024)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Sawai Madhopur 2024', width=450, height=280)


</code>
"
4263,specific_pattern,Show a cumulative area chart of PM2.5 readings for Nandesari across 2022.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Nandesari') & (data['Timestamp'].dt.year == 2022)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Nandesari 2022', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Nandesari') & (data['Timestamp'].dt.year == 2022)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Nandesari 2022', width=600, height=300)
     return chart


</code>
"
4264,temporal_aggregation,Show the monthly average PM10 trend for Tirupati from 2017 to 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Tirupati'].copy()
    df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Tirupati (2017–2022)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Tirupati'].copy()
     df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Tirupati (2017–2022)', width=600, height=300)
     return chart


</code>
"
4265,specific_pattern,Show a cumulative area chart of PM2.5 readings for Nagaur across 2023.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Nagaur') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Nagaur 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Nagaur') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Nagaur 2023', width=600, height=300)
     return chart


</code>
"
4266,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Punjab, Uttar Pradesh, and Jharkhand from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Punjab', 'Uttar Pradesh', 'Jharkhand']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Punjab vs Uttar Pradesh vs Jharkhand', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Punjab', 'Uttar Pradesh', 'Jharkhand']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Punjab vs Uttar Pradesh vs Jharkhand', width=550, height=320)
     return chart


</code>
"
4267,temporal_aggregation,Show the monthly average PM10 trend for Virar from 2019 to 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Virar'].copy()
    df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Virar (2019–2024)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Virar'].copy()
     df = df[(df['Timestamp'].dt.year >= 2019) & (df['Timestamp'].dt.year <= 2024)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Virar (2019–2024)', width=600, height=300)
     return chart


</code>
"
4268,temporal_aggregation,Show the monthly average PM2.5 for Damoh in 2017 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Damoh') & (data['Timestamp'].dt.year == 2017)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Damoh 2017', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Damoh') & (data['Timestamp'].dt.year == 2017)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Damoh 2017', width=450, height=280)


</code>
"
4269,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Chandigarh, West Bengal, and Meghalaya in 2019 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Chandigarh', 'West Bengal', 'Meghalaya']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Chandigarh, West Bengal, UP – 2019', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Chandigarh', 'West Bengal', 'Meghalaya']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Chandigarh, West Bengal, UP – 2019', width=550, height=320)
     return chart


</code>
"
4270,specific_pattern,Plot the rolling 30-day average PM2.5 for Manipur in 2017 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Manipur') & (data['Timestamp'].dt.year == 2017)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Manipur 2017', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Manipur') & (data['Timestamp'].dt.year == 2017)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Manipur 2017', width=600, height=300)


</code>
"
4271,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Bhagalpur, Raichur, and Sonipat in 2019 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Bhagalpur', 'Raichur', 'Sonipat']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Bhagalpur vs Raichur vs Sonipat – 2019', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Bhagalpur', 'Raichur', 'Sonipat']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Bhagalpur vs Raichur vs Sonipat – 2019', width=550, height=320)
     return chart


</code>
"
4272,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Gujarat, Telangana, and Himachal Pradesh from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Gujarat', 'Telangana', 'Himachal Pradesh']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Gujarat vs Telangana vs Himachal Pradesh', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Gujarat', 'Telangana', 'Himachal Pradesh']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Gujarat vs Telangana vs Himachal Pradesh', width=550, height=320)
     return chart


</code>
"
4273,temporal_aggregation,Plot the weekly average PM2.5 for Sonipat in 2021 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Sonipat') & (data['Timestamp'].dt.year == 2021)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Sonipat 2021', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Sonipat') & (data['Timestamp'].dt.year == 2021)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Sonipat 2021', width=600, height=300)
     return chart


</code>
"
4274,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Arunachal Pradesh, Jammu and Kashmir, and West Bengal in 2019 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Arunachal Pradesh', 'Jammu and Kashmir', 'West Bengal']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Arunachal Pradesh, Jammu and Kashmir, UP – 2019', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Arunachal Pradesh', 'Jammu and Kashmir', 'West Bengal']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Arunachal Pradesh, Jammu and Kashmir, UP – 2019', width=550, height=320)
     return chart


</code>
"
4275,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Angul, Perundurai, and Chennai in 2020 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Angul', 'Perundurai', 'Chennai']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2020)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Angul vs Perundurai vs Chennai – 2020', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Angul', 'Perundurai', 'Chennai']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2020)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Angul vs Perundurai vs Chennai – 2020', width=550, height=320)
     return chart


</code>
"
4276,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Karnataka, Jharkhand, and Kerala from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Karnataka', 'Jharkhand', 'Kerala']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Karnataka vs Jharkhand vs Kerala', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Karnataka', 'Jharkhand', 'Kerala']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Karnataka vs Jharkhand vs Kerala', width=550, height=320)
     return chart


</code>
"
4277,temporal_aggregation,Plot the weekly average PM2.5 for Rajamahendravaram in 2021 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Rajamahendravaram') & (data['Timestamp'].dt.year == 2021)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Rajamahendravaram 2021', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Rajamahendravaram') & (data['Timestamp'].dt.year == 2021)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Rajamahendravaram 2021', width=600, height=300)
     return chart


</code>
"
4278,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Odisha, Tamil Nadu, and Nagaland in 2017 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Odisha', 'Tamil Nadu', 'Nagaland']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2017)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Odisha, Tamil Nadu, UP – 2017', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Odisha', 'Tamil Nadu', 'Nagaland']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2017)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Odisha, Tamil Nadu, UP – 2017', width=550, height=320)
     return chart


</code>
"
4279,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Raichur, Barrackpore, and Navi Raichur in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Raichur', 'Barrackpore', 'Navi Raichur']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Raichur vs Barrackpore vs Navi Raichur – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Raichur', 'Barrackpore', 'Navi Raichur']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Raichur vs Barrackpore vs Navi Raichur – 2024', width=550, height=320)
     return chart


</code>
"
4280,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Jammu and Kashmir, Mizoram, and Chhattisgarh in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Jammu and Kashmir', 'Mizoram', 'Chhattisgarh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Jammu and Kashmir, Mizoram, UP – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Jammu and Kashmir', 'Mizoram', 'Chhattisgarh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Jammu and Kashmir, Mizoram, UP – 2024', width=550, height=320)
     return chart


</code>
"
4281,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Karnal, Silchar, and Shillong in 2020 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Karnal', 'Silchar', 'Shillong']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2020)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Karnal vs Silchar vs Shillong – 2020', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Karnal', 'Silchar', 'Shillong']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2020)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Karnal vs Silchar vs Shillong – 2020', width=550, height=320)
     return chart


</code>
"
4282,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Chhattisgarh, Odisha, and Assam in 2021 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Chhattisgarh', 'Odisha', 'Assam']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Chhattisgarh, Odisha, UP – 2021', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Chhattisgarh', 'Odisha', 'Assam']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2021)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Chhattisgarh, Odisha, UP – 2021', width=550, height=320)
     return chart


</code>
"
4283,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Manipur, Madhya Pradesh, and Haryana from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Manipur', 'Madhya Pradesh', 'Haryana']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Manipur vs Madhya Pradesh vs Haryana', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Manipur', 'Madhya Pradesh', 'Haryana']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Manipur vs Madhya Pradesh vs Haryana', width=550, height=320)
     return chart


</code>
"
4284,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Chandigarh, Chhattisgarh, and Rajasthan from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Chandigarh', 'Chhattisgarh', 'Rajasthan']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Chandigarh vs Chhattisgarh vs Rajasthan', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Chandigarh', 'Chhattisgarh', 'Rajasthan']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Chandigarh vs Chhattisgarh vs Rajasthan', width=550, height=320)
     return chart


</code>
"
4285,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Karnataka, Meghalaya, and Jammu and Kashmir from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Karnataka', 'Meghalaya', 'Jammu and Kashmir']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Karnataka vs Meghalaya vs Jammu and Kashmir', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Karnataka', 'Meghalaya', 'Jammu and Kashmir']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Karnataka vs Meghalaya vs Jammu and Kashmir', width=550, height=320)
     return chart


</code>
"
4286,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Kerala, Sikkim, and Jammu and Kashmir in 2017 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Kerala', 'Sikkim', 'Jammu and Kashmir']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2017)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Kerala, Sikkim, UP – 2017', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Kerala', 'Sikkim', 'Jammu and Kashmir']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2017)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Kerala, Sikkim, UP – 2017', width=550, height=320)
     return chart


</code>
"
4287,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Tamil Nadu, Jharkhand, and Tamil Nadu in 2018 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Tamil Nadu', 'Jharkhand', 'Tamil Nadu']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2018)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Tamil Nadu, Jharkhand, UP – 2018', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Tamil Nadu', 'Jharkhand', 'Tamil Nadu']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2018)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Tamil Nadu, Jharkhand, UP – 2018', width=550, height=320)
     return chart


</code>
"
4288,temporal_aggregation,Show the monthly average PM2.5 for Mysuru in 2023 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Mysuru') & (data['Timestamp'].dt.year == 2023)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Mysuru 2023', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Mysuru') & (data['Timestamp'].dt.year == 2023)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Mysuru 2023', width=450, height=280)


</code>
"
4289,specific_pattern,Show a cumulative area chart of PM2.5 readings for Sawai Madhopur across 2024.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Sawai Madhopur') & (data['Timestamp'].dt.year == 2024)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Sawai Madhopur 2024', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Sawai Madhopur') & (data['Timestamp'].dt.year == 2024)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Sawai Madhopur 2024', width=600, height=300)
     return chart


</code>
"
4290,temporal_aggregation,Show the monthly average PM2.5 for Prayagraj in 2020 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Prayagraj') & (data['Timestamp'].dt.year == 2020)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Prayagraj 2020', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Prayagraj') & (data['Timestamp'].dt.year == 2020)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Prayagraj 2020', width=450, height=280)


</code>
"
4291,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Uttarakhand, Mizoram, and Maharashtra in 2017 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Uttarakhand', 'Mizoram', 'Maharashtra']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2017)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Uttarakhand, Mizoram, UP – 2017', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Uttarakhand', 'Mizoram', 'Maharashtra']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2017)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Uttarakhand, Mizoram, UP – 2017', width=550, height=320)
     return chart


</code>
"
4292,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Tripura, Kerala, and Karnataka in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Tripura', 'Kerala', 'Karnataka']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Tripura, Kerala, UP – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Tripura', 'Kerala', 'Karnataka']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Tripura, Kerala, UP – 2024', width=550, height=320)
     return chart


</code>
"
4293,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Punjab, West Bengal, and Chhattisgarh from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Punjab', 'West Bengal', 'Chhattisgarh']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Punjab vs West Bengal vs Chhattisgarh', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Punjab', 'West Bengal', 'Chhattisgarh']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Punjab vs West Bengal vs Chhattisgarh', width=550, height=320)
     return chart


</code>
"
4294,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Bihar, Madhya Pradesh, and Assam across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Bihar', 'Madhya Pradesh', 'Assam']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Bihar', 'Madhya Pradesh', 'Assam']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
4295,temporal_aggregation,Plot the weekly average PM2.5 for Vijayapura in 2021 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Vijayapura') & (data['Timestamp'].dt.year == 2021)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Vijayapura 2021', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Vijayapura') & (data['Timestamp'].dt.year == 2021)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Vijayapura 2021', width=600, height=300)
     return chart


</code>
"
4296,temporal_aggregation,Show the monthly average PM2.5 for Vellore in 2023 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Vellore') & (data['Timestamp'].dt.year == 2023)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Vellore 2023', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Vellore') & (data['Timestamp'].dt.year == 2023)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Vellore 2023', width=450, height=280)


</code>
"
4297,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Tripura, West Bengal, and Nagaland across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Tripura', 'West Bengal', 'Nagaland']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Tripura', 'West Bengal', 'Nagaland']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
4298,temporal_aggregation,Show the monthly average PM2.5 for Baghpat in 2024 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Baghpat') & (data['Timestamp'].dt.year == 2024)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Baghpat 2024', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Baghpat') & (data['Timestamp'].dt.year == 2024)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Baghpat 2024', width=450, height=280)


</code>
"
4299,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Tamil Nadu, Madhya Pradesh, and Andhra Pradesh in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Tamil Nadu', 'Madhya Pradesh', 'Andhra Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Tamil Nadu, Madhya Pradesh, UP – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Tamil Nadu', 'Madhya Pradesh', 'Andhra Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Tamil Nadu, Madhya Pradesh, UP – 2022', width=550, height=320)
     return chart


</code>
"
4300,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Prayagraj, Gummidipoondi, and Sri Ganganagar in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Prayagraj', 'Gummidipoondi', 'Sri Ganganagar']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Prayagraj vs Gummidipoondi vs Sri Ganganagar – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Prayagraj', 'Gummidipoondi', 'Sri Ganganagar']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Prayagraj vs Gummidipoondi vs Sri Ganganagar – 2023', width=550, height=320)
     return chart


</code>
"
4301,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Chhattisgarh, Meghalaya, and Delhi across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Chhattisgarh', 'Meghalaya', 'Delhi']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Chhattisgarh', 'Meghalaya', 'Delhi']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
4302,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Gujarat, Himachal Pradesh, and Odisha across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Gujarat', 'Himachal Pradesh', 'Odisha']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Gujarat', 'Himachal Pradesh', 'Odisha']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
4303,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Odisha, Kerala, and Jharkhand in 2019 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Odisha', 'Kerala', 'Jharkhand']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Odisha, Kerala, UP – 2019', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Odisha', 'Kerala', 'Jharkhand']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Odisha, Kerala, UP – 2019', width=550, height=320)
     return chart


</code>
"
4304,specific_pattern,Show a cumulative area chart of PM2.5 readings for Surat across 2024.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Surat') & (data['Timestamp'].dt.year == 2024)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Surat 2024', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Surat') & (data['Timestamp'].dt.year == 2024)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Surat 2024', width=600, height=300)
     return chart


</code>
"
4305,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Tamil Nadu, Assam, and Sikkim in 2019 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Tamil Nadu', 'Assam', 'Sikkim']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Tamil Nadu, Assam, UP – 2019', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Tamil Nadu', 'Assam', 'Sikkim']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Tamil Nadu, Assam, UP – 2019', width=550, height=320)
     return chart


</code>
"
4306,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Jalna, Palwal, and Panipat in 2019 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Jalna', 'Palwal', 'Panipat']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Jalna vs Palwal vs Panipat – 2019', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Jalna', 'Palwal', 'Panipat']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Jalna vs Palwal vs Panipat – 2019', width=550, height=320)
     return chart


</code>
"
4307,temporal_aggregation,Plot the weekly average PM2.5 for Vatva in 2021 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Vatva') & (data['Timestamp'].dt.year == 2021)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Vatva 2021', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Vatva') & (data['Timestamp'].dt.year == 2021)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Vatva 2021', width=600, height=300)
     return chart


</code>
"
4308,temporal_aggregation,Plot the weekly average PM2.5 for Solapur in 2017 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Solapur') & (data['Timestamp'].dt.year == 2017)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Solapur 2017', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Solapur') & (data['Timestamp'].dt.year == 2017)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Solapur 2017', width=600, height=300)
     return chart


</code>
"
4309,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Uttar Pradesh, Gujarat, and Andhra Pradesh from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Uttar Pradesh', 'Gujarat', 'Andhra Pradesh']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Uttar Pradesh vs Gujarat vs Andhra Pradesh', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Uttar Pradesh', 'Gujarat', 'Andhra Pradesh']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Uttar Pradesh vs Gujarat vs Andhra Pradesh', width=550, height=320)
     return chart


</code>
"
4310,specific_pattern,Plot the rolling 30-day average PM2.5 for Meghalaya in 2018 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Meghalaya') & (data['Timestamp'].dt.year == 2018)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Meghalaya 2018', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Meghalaya') & (data['Timestamp'].dt.year == 2018)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Meghalaya 2018', width=600, height=300)


</code>
"
4311,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Bihar, Chandigarh, and Bihar from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Bihar', 'Chandigarh', 'Bihar']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Bihar vs Chandigarh vs Bihar', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Bihar', 'Chandigarh', 'Bihar']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Bihar vs Chandigarh vs Bihar', width=550, height=320)
     return chart


</code>
"
4312,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Assam, Jammu and Kashmir, and Sikkim from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Assam', 'Jammu and Kashmir', 'Sikkim']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Assam vs Jammu and Kashmir vs Sikkim', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Assam', 'Jammu and Kashmir', 'Sikkim']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Assam vs Jammu and Kashmir vs Sikkim', width=550, height=320)
     return chart


</code>
"
4313,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Uttar Pradesh, Chhattisgarh, and Haryana across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Uttar Pradesh', 'Chhattisgarh', 'Haryana']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Uttar Pradesh', 'Chhattisgarh', 'Haryana']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
4314,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Mahad, Perundurai, and Bhiwadi in 2020 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Mahad', 'Perundurai', 'Bhiwadi']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2020)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Mahad vs Perundurai vs Bhiwadi – 2020', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Mahad', 'Perundurai', 'Bhiwadi']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2020)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Mahad vs Perundurai vs Bhiwadi – 2020', width=550, height=320)
     return chart


</code>
"
4315,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Faridabad, Nagaon, and Bileipada in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Faridabad', 'Nagaon', 'Bileipada']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Faridabad vs Nagaon vs Bileipada – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Faridabad', 'Nagaon', 'Bileipada']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Faridabad vs Nagaon vs Bileipada – 2024', width=550, height=320)
     return chart


</code>
"
4316,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Himachal Pradesh, Jharkhand, and West Bengal across 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Himachal Pradesh', 'Jharkhand', 'West Bengal']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Himachal Pradesh', 'Jharkhand', 'West Bengal']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
     return chart


</code>
"
4317,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Uttarakhand, Telangana, and Rajasthan from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Uttarakhand', 'Telangana', 'Rajasthan']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Uttarakhand vs Telangana vs Rajasthan', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Uttarakhand', 'Telangana', 'Rajasthan']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Uttarakhand vs Telangana vs Rajasthan', width=550, height=320)
     return chart


</code>
"
4318,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Andhra Pradesh, Delhi, and Nagaland across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Andhra Pradesh', 'Delhi', 'Nagaland']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Andhra Pradesh', 'Delhi', 'Nagaland']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
4319,temporal_aggregation,Show the monthly average PM2.5 for Jalna in 2020 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Jalna') & (data['Timestamp'].dt.year == 2020)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Jalna 2020', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Jalna') & (data['Timestamp'].dt.year == 2020)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Jalna 2020', width=450, height=280)


</code>
"
4320,temporal_aggregation,Show a monthly bar chart of the number of days Uttar Pradesh exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2020.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Uttar Pradesh') & (data['Timestamp'].dt.year == 2020)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Uttar Pradesh Exceeded WHO PM2.5 Guideline per Month – 2020', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Uttar Pradesh') & (data['Timestamp'].dt.year == 2020)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Uttar Pradesh Exceeded WHO PM2.5 Guideline per Month – 2020', width=500, height=300)
     return chart


</code>
"
4321,specific_pattern,Plot the rolling 30-day average PM2.5 for Himachal Pradesh in 2017 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Himachal Pradesh') & (data['Timestamp'].dt.year == 2017)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Himachal Pradesh 2017', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Himachal Pradesh') & (data['Timestamp'].dt.year == 2017)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Himachal Pradesh 2017', width=600, height=300)


</code>
"
4322,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Tripura, Uttar Pradesh, and Sikkim across 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Tripura', 'Uttar Pradesh', 'Sikkim']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Tripura', 'Uttar Pradesh', 'Sikkim']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
     return chart


</code>
"
4323,temporal_aggregation,Show a monthly bar chart of the number of days Punjab exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2021.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Punjab') & (data['Timestamp'].dt.year == 2021)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Punjab Exceeded WHO PM2.5 Guideline per Month – 2021', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Punjab') & (data['Timestamp'].dt.year == 2021)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Punjab Exceeded WHO PM2.5 Guideline per Month – 2021', width=500, height=300)
     return chart


</code>
"
4324,temporal_aggregation,Show the monthly average PM2.5 for Dausa in 2019 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Dausa') & (data['Timestamp'].dt.year == 2019)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Dausa 2019', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Dausa') & (data['Timestamp'].dt.year == 2019)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Dausa 2019', width=450, height=280)


</code>
"
4325,temporal_aggregation,Show the monthly average PM2.5 for Pune in 2018 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Pune') & (data['Timestamp'].dt.year == 2018)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Pune 2018', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Pune') & (data['Timestamp'].dt.year == 2018)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Pune 2018', width=450, height=280)


</code>
"
4326,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Maharashtra, Kerala, and Chhattisgarh in 2018 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Maharashtra', 'Kerala', 'Chhattisgarh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2018)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Maharashtra, Kerala, UP – 2018', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Maharashtra', 'Kerala', 'Chhattisgarh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2018)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Maharashtra, Kerala, UP – 2018', width=550, height=320)
     return chart


</code>
"
4327,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Kerala, Chandigarh, and Chandigarh in 2018 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Kerala', 'Chandigarh', 'Chandigarh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2018)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Kerala, Chandigarh, UP – 2018', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Kerala', 'Chandigarh', 'Chandigarh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2018)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Kerala, Chandigarh, UP – 2018', width=550, height=320)
     return chart


</code>
"
4328,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Himachal Pradesh, Uttar Pradesh, and Kerala across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Himachal Pradesh', 'Uttar Pradesh', 'Kerala']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Himachal Pradesh', 'Uttar Pradesh', 'Kerala']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
4329,temporal_aggregation,Show the monthly average PM2.5 for Sirohi in 2020 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Sirohi') & (data['Timestamp'].dt.year == 2020)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Sirohi 2020', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Sirohi') & (data['Timestamp'].dt.year == 2020)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Sirohi 2020', width=450, height=280)


</code>
"
4330,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Odisha, Delhi, and Manipur across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Odisha', 'Delhi', 'Manipur']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Odisha', 'Delhi', 'Manipur']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
4331,temporal_aggregation,Plot the weekly average PM2.5 for Boisar in 2024 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Boisar') & (data['Timestamp'].dt.year == 2024)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Boisar 2024', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Boisar') & (data['Timestamp'].dt.year == 2024)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Boisar 2024', width=600, height=300)
     return chart


</code>
"
4332,temporal_aggregation,Show the monthly average PM2.5 for Kurukshetra in 2017 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Kurukshetra') & (data['Timestamp'].dt.year == 2017)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Kurukshetra 2017', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Kurukshetra') & (data['Timestamp'].dt.year == 2017)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Kurukshetra 2017', width=450, height=280)


</code>
"
4333,temporal_aggregation,Show the monthly average PM10 trend for Chamarajanagar from 2017 to 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Chamarajanagar'].copy()
    df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Chamarajanagar (2017–2022)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Chamarajanagar'].copy()
     df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Chamarajanagar (2017–2022)', width=600, height=300)
     return chart


</code>
"
4334,specific_pattern,Show a cumulative area chart of PM2.5 readings for Muzaffarpur across 2023.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Muzaffarpur') & (data['Timestamp'].dt.year == 2023)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Muzaffarpur 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Muzaffarpur') & (data['Timestamp'].dt.year == 2023)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Muzaffarpur 2023', width=600, height=300)
     return chart


</code>
"
4335,temporal_aggregation,Show the monthly average PM2.5 for Bulandshahr in 2017 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Bulandshahr') & (data['Timestamp'].dt.year == 2017)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Bulandshahr 2017', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Bulandshahr') & (data['Timestamp'].dt.year == 2017)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Bulandshahr 2017', width=450, height=280)


</code>
"
4336,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Meghalaya, Maharashtra, and Uttarakhand in 2022 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Meghalaya', 'Maharashtra', 'Uttarakhand']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Meghalaya, Maharashtra, UP – 2022', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Meghalaya', 'Maharashtra', 'Uttarakhand']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2022)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Meghalaya, Maharashtra, UP – 2022', width=550, height=320)
     return chart


</code>
"
4337,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Bhilwara, Kunjemura, and Bhiwadi in 2024 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Bhilwara', 'Kunjemura', 'Bhiwadi']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Bhilwara vs Kunjemura vs Bhiwadi – 2024', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Bhilwara', 'Kunjemura', 'Bhiwadi']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2024)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Bhilwara vs Kunjemura vs Bhiwadi – 2024', width=550, height=320)
     return chart


</code>
"
4338,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Manipur, Tripura, and Kerala across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Manipur', 'Tripura', 'Kerala']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Manipur', 'Tripura', 'Kerala']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
4339,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Jharkhand, Karnataka, and Arunachal Pradesh in 2023 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Jharkhand', 'Karnataka', 'Arunachal Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Jharkhand, Karnataka, UP – 2023', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Jharkhand', 'Karnataka', 'Arunachal Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2023)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Jharkhand, Karnataka, UP – 2023', width=550, height=320)
     return chart


</code>
"
4340,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for West Bengal, Sikkim, and Tamil Nadu across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['West Bengal', 'Sikkim', 'Tamil Nadu']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['West Bengal', 'Sikkim', 'Tamil Nadu']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
4341,spatio_temporal_aggregation,"Visualize the monthly average PM10 for West Bengal, Arunachal Pradesh, and Kerala in 2019 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['West Bengal', 'Arunachal Pradesh', 'Kerala']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: West Bengal, Arunachal Pradesh, UP – 2019', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['West Bengal', 'Arunachal Pradesh', 'Kerala']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: West Bengal, Arunachal Pradesh, UP – 2019', width=550, height=320)
     return chart


</code>
"
4342,temporal_aggregation,Show the monthly average PM10 trend for Bareilly from 2017 to 2022 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[data['city'] == 'Bareilly'].copy()
    df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
    df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
    df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='darkorange').encode(
        x=alt.X('YearMonth:T', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                 alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly Average PM10 Trend – Bareilly (2017–2022)', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[data['city'] == 'Bareilly'].copy()
     df = df[(df['Timestamp'].dt.year >= 2017) & (df['Timestamp'].dt.year <= 2022)]
     df['YearMonth'] = df['Timestamp'].dt.to_period('M').dt.to_timestamp()
     df = df.groupby('YearMonth')['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='darkorange').encode(
         x=alt.X('YearMonth:T', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         tooltip=[alt.Tooltip('YearMonth:T', title='Month', format='%b %Y'),
                  alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly Average PM10 Trend – Bareilly (2017–2022)', width=600, height=300)
     return chart


</code>
"
4343,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Kerala, Jharkhand, and Uttarakhand across 2021, 2022, 2023, and 2024.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Kerala', 'Jharkhand', 'Uttarakhand']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Kerala', 'Jharkhand', 'Uttarakhand']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2021,2022,2023,2024]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2021–2024)', width=500, height=320)
     return chart


</code>
"
4344,temporal_aggregation,Show the monthly average PM2.5 for Ajmer in 2022 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Ajmer') & (data['Timestamp'].dt.year == 2022)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Ajmer 2022', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Ajmer') & (data['Timestamp'].dt.year == 2022)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Ajmer 2022', width=450, height=280)


</code>
"
4345,temporal_aggregation,Plot the weekly average PM2.5 for Chikkaballapur in 2018 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Chikkaballapur') & (data['Timestamp'].dt.year == 2018)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Chikkaballapur 2018', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Chikkaballapur') & (data['Timestamp'].dt.year == 2018)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Chikkaballapur 2018', width=600, height=300)
     return chart


</code>
"
4346,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Gujarat, Karnataka, and Bihar from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Gujarat', 'Karnataka', 'Bihar']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Gujarat vs Karnataka vs Bihar', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Gujarat', 'Karnataka', 'Bihar']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Gujarat vs Karnataka vs Bihar', width=550, height=320)
     return chart


</code>
"
4347,specific_pattern,Show a cumulative area chart of PM2.5 readings for Chamarajanagar across 2021.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Chamarajanagar') & (data['Timestamp'].dt.year == 2021)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
    df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
    chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
    ).properties(title='Cumulative PM2.5 – Chamarajanagar 2021', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Chamarajanagar') & (data['Timestamp'].dt.year == 2021)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna().sort_values('Timestamp')
     df['Cumulative PM2.5'] = df['PM2.5'].cumsum()
     chart = alt.Chart(df).mark_area(color='teal', opacity=0.5).encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('Cumulative PM2\.5:Q', title='Cumulative PM2.5 (µg/m³)'),
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b'), alt.Tooltip('Cumulative PM2\.5:Q', format='.0f')]
     ).properties(title='Cumulative PM2.5 – Chamarajanagar 2021', width=600, height=300)
     return chart


</code>
"
4348,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Odisha, Chhattisgarh, and Jharkhand from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Odisha', 'Chhattisgarh', 'Jharkhand']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Odisha vs Chhattisgarh vs Jharkhand', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Odisha', 'Chhattisgarh', 'Jharkhand']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Odisha vs Chhattisgarh vs Jharkhand', width=550, height=320)
     return chart


</code>
"
4349,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Jharkhand, Haryana, and Chhattisgarh across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Jharkhand', 'Haryana', 'Chhattisgarh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Jharkhand', 'Haryana', 'Chhattisgarh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
4350,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Chhattisgarh, Mizoram, and Himachal Pradesh across 2017, 2018, 2019, and 2020.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Chhattisgarh', 'Mizoram', 'Himachal Pradesh']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Chhattisgarh', 'Mizoram', 'Himachal Pradesh']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2017,2018,2019,2020]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2017–2020)', width=500, height=320)
     return chart


</code>
"
4351,temporal_aggregation,Show the monthly average PM2.5 for Chengalpattu in 2018 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Chengalpattu') & (data['Timestamp'].dt.year == 2018)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Chengalpattu 2018', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Chengalpattu') & (data['Timestamp'].dt.year == 2018)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Chengalpattu 2018', width=450, height=280)


</code>
"
4352,temporal_aggregation,Show a monthly bar chart of the number of days Bihar exceeded the WHO PM2.5 guideline (15 µg/m³) per year in 2021.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Bihar') & (data['Timestamp'].dt.year == 2021)]
    df = df.dropna(subset=['PM2.5'])
    df = df[df['PM2.5'] > 15]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['Timestamp'].nunique().reset_index()
    df.columns = ['Month','Days Exceeded']
    month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                   7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
    df['MonthName'] = df['Month'].map(month_names)
    chart = alt.Chart(df).mark_bar(color='crimson').encode(
        x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
        y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
        tooltip=['MonthName:N','Days Exceeded:Q']
    ).properties(title='Days Bihar Exceeded WHO PM2.5 Guideline per Month – 2021', width=500, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Bihar') & (data['Timestamp'].dt.year == 2021)]
     df = df.dropna(subset=['PM2.5'])
     df = df[df['PM2.5'] > 15]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['Timestamp'].nunique().reset_index()
     df.columns = ['Month','Days Exceeded']
     month_names = {1:'Jan',2:'Feb',3:'Mar',4:'Apr',5:'May',6:'Jun',
                    7:'Jul',8:'Aug',9:'Sep',10:'Oct',11:'Nov',12:'Dec'}
     df['MonthName'] = df['Month'].map(month_names)
     chart = alt.Chart(df).mark_bar(color='crimson').encode(
         x=alt.X('MonthName:N', sort=list(month_names.values()), title='Month'),
         y=alt.Y('Days Exceeded:Q', title='Days Exceeding WHO Limit'),
         tooltip=['MonthName:N','Days Exceeded:Q']
     ).properties(title='Days Bihar Exceeded WHO PM2.5 Guideline per Month – 2021', width=500, height=300)
     return chart


</code>
"
4353,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Bihar, Maharashtra, and Jammu and Kashmir in 2017 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Bihar', 'Maharashtra', 'Jammu and Kashmir']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2017)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Bihar, Maharashtra, UP – 2017', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Bihar', 'Maharashtra', 'Jammu and Kashmir']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2017)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Bihar, Maharashtra, UP – 2017', width=550, height=320)
     return chart


</code>
"
4354,temporal_aggregation,Show the monthly average PM2.5 for Ahmednagar in 2019 as a line chart with area fill.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Ahmednagar') & (data['Timestamp'].dt.year == 2019)]
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
    area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
    )
    line = alt.Chart(df).mark_line(color='teal').encode(
        x='Month:O', y='PM2\.5:Q',
        tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    )
    return (area + line).properties(title='Monthly Average PM2.5 – Ahmednagar 2019', width=450, height=280)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Ahmednagar') & (data['Timestamp'].dt.year == 2019)]
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby('Month')['PM2.5'].mean().reset_index().dropna()
     area = alt.Chart(df).mark_area(opacity=0.3, color='teal').encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)')
     )
     line = alt.Chart(df).mark_line(color='teal').encode(
         x='Month:O', y='PM2\.5:Q',
         tooltip=['Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     )
     return (area + line).properties(title='Monthly Average PM2.5 – Ahmednagar 2019', width=450, height=280)


</code>
"
4355,temporal_aggregation,Plot the weekly average PM2.5 for Asansol in 2023 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['city'] == 'Asansol') & (data['Timestamp'].dt.year == 2023)].copy()
    df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
    df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(color='purple').encode(
        x=alt.X('Week:O', title='Week of Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Weekly Average PM2.5 – Asansol 2023', width=600, height=300)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['city'] == 'Asansol') & (data['Timestamp'].dt.year == 2023)].copy()
     df['Week'] = df['Timestamp'].dt.isocalendar().week.astype(int)
     df = df.groupby('Week')['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(color='purple').encode(
         x=alt.X('Week:O', title='Week of Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         tooltip=['Week:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Weekly Average PM2.5 – Asansol 2023', width=600, height=300)
     return chart


</code>
"
4356,specific_pattern,Plot the rolling 30-day average PM2.5 for Madhya Pradesh in 2018 as a line chart.,"import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    df = data[(data['state'] == 'Madhya Pradesh') & (data['Timestamp'].dt.year == 2018)]
    df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
    df = df.sort_values('Timestamp')
    df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
    raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
        x=alt.X('Timestamp:T', title='Date'),
        y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
    )
    smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
        x='Timestamp:T', y='Rolling30:Q',
        tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                 alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
    )
    return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Madhya Pradesh 2018', width=600, height=300)","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     df = data[(data['state'] == 'Madhya Pradesh') & (data['Timestamp'].dt.year == 2018)]
     df = df.groupby('Timestamp')['PM2.5'].mean().reset_index().dropna()
     df = df.sort_values('Timestamp')
     df['Rolling30'] = df['PM2.5'].rolling(30, min_periods=1).mean()
     raw = alt.Chart(df).mark_line(opacity=0.3, color='steelblue').encode(
         x=alt.X('Timestamp:T', title='Date'),
         y=alt.Y('PM2\.5:Q', title='PM2.5 (µg/m³)')
     )
     smooth = alt.Chart(df).mark_line(color='firebrick', strokeWidth=2).encode(
         x='Timestamp:T', y='Rolling30:Q',
         tooltip=[alt.Tooltip('Timestamp:T', format='%d %b %Y'),
                  alt.Tooltip('Rolling30:Q', format='.1f', title='30-day avg')]
     )
     return (raw + smooth).properties(title='30-Day Rolling Average PM2.5 – Madhya Pradesh 2018', width=600, height=300)


</code>
"
4357,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Mizoram, Maharashtra, and Karnataka in 2019 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Mizoram', 'Maharashtra', 'Karnataka']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Mizoram, Maharashtra, UP – 2019', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Mizoram', 'Maharashtra', 'Karnataka']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Mizoram, Maharashtra, UP – 2019', width=550, height=320)
     return chart


</code>
"
4358,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Andhra Pradesh, Mizoram, and Assam across 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Andhra Pradesh', 'Mizoram', 'Assam']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Andhra Pradesh', 'Mizoram', 'Assam']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
     return chart


</code>
"
4359,spatio_temporal_aggregation,"Compare the monthly average PM2.5 of Ooty, Khanna, and Kunjemura in 2020 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    cities = ['Ooty', 'Khanna', 'Kunjemura']
    df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2020)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('city:N', title='City'),
        tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Monthly PM2.5 Comparison: Ooty vs Khanna vs Kunjemura – 2020', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     cities = ['Ooty', 'Khanna', 'Kunjemura']
     df = data[(data['city'].isin(cities)) & (data['Timestamp'].dt.year == 2020)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','city'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('city:N', title='City'),
         tooltip=['city:N','Month:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Monthly PM2.5 Comparison: Ooty vs Khanna vs Kunjemura – 2020', width=550, height=320)
     return chart


</code>
"
4360,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Haryana, Sikkim, and Uttarakhand across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Haryana', 'Sikkim', 'Uttarakhand']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Haryana', 'Sikkim', 'Uttarakhand']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
4361,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Chandigarh, Assam, and Rajasthan across 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Chandigarh', 'Assam', 'Rajasthan']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Chandigarh', 'Assam', 'Rajasthan']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
     return chart


</code>
"
4362,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Manipur, West Bengal, and Tamil Nadu from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Manipur', 'West Bengal', 'Tamil Nadu']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Manipur vs West Bengal vs Tamil Nadu', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Manipur', 'West Bengal', 'Tamil Nadu']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Manipur vs West Bengal vs Tamil Nadu', width=550, height=320)
     return chart


</code>
"
4363,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Punjab, Uttarakhand, and Kerala in 2019 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Punjab', 'Uttarakhand', 'Kerala']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Punjab, Uttarakhand, UP – 2019', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Punjab', 'Uttarakhand', 'Kerala']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Punjab, Uttarakhand, UP – 2019', width=550, height=320)
     return chart


</code>
"
4364,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Mizoram, Jammu and Kashmir, and Tamil Nadu across 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Mizoram', 'Jammu and Kashmir', 'Tamil Nadu']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Mizoram', 'Jammu and Kashmir', 'Tamil Nadu']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
     return chart


</code>
"
4365,spatio_temporal_aggregation,"Plot the yearly average PM2.5 trends for Odisha, Himachal Pradesh, and Puducherry from 2017 to 2024 on a single multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Odisha', 'Himachal Pradesh', 'Puducherry']
    df = data[data['state'].isin(states)].copy()
    df['Year'] = df['Timestamp'].dt.year
    df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Year:O', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='Yearly PM2.5 Trends: Odisha vs Himachal Pradesh vs Puducherry', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Odisha', 'Himachal Pradesh', 'Puducherry']
     df = data[data['state'].isin(states)].copy()
     df['Year'] = df['Timestamp'].dt.year
     df = df.groupby(['Year','state'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Year:O', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Year:O', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='Yearly PM2.5 Trends: Odisha vs Himachal Pradesh vs Puducherry', width=550, height=320)
     return chart


</code>
"
4366,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Odisha, Sikkim, and Bihar across 2019, 2020, 2021, and 2022.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Odisha', 'Sikkim', 'Bihar']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Odisha', 'Sikkim', 'Bihar']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2019,2020,2021,2022]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2019–2022)', width=500, height=320)
     return chart


</code>
"
4367,spatio_temporal_aggregation,"Create a grouped bar chart comparing the average PM2.5 for Madhya Pradesh, Karnataka, and Rajasthan across 2020, 2021, 2022, and 2023.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Madhya Pradesh', 'Karnataka', 'Rajasthan']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
    df = df.copy()
    df['Year'] = df['Timestamp'].dt.year.astype(str)
    df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_bar().encode(
        x=alt.X('Year:N', title='Year'),
        y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        xOffset='state:N',
        tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
    ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Madhya Pradesh', 'Karnataka', 'Rajasthan']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year.isin([2020,2021,2022,2023]))]
     df = df.copy()
     df['Year'] = df['Timestamp'].dt.year.astype(str)
     df = df.groupby(['state','Year'])['PM2.5'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_bar().encode(
         x=alt.X('Year:N', title='Year'),
         y=alt.Y('PM2\.5:Q', title='Average PM2.5 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         xOffset='state:N',
         tooltip=['state:N','Year:N', alt.Tooltip('PM2\.5:Q', format='.1f')]
     ).properties(title='PM2.5 Grouped by State and Year (2020–2023)', width=500, height=320)
     return chart


</code>
"
4368,spatio_temporal_aggregation,"Visualize the monthly average PM10 for Gujarat, Gujarat, and Jharkhand in 2019 as a multi-line chart.","import pandas as pd
import altair as alt
def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
    states = ['Gujarat', 'Gujarat', 'Jharkhand']
    df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
    df['Month'] = df['Timestamp'].dt.month
    df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
    chart = alt.Chart(df).mark_line(point=True).encode(
        x=alt.X('Month:O', title='Month'),
        y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
        color=alt.Color('state:N', title='State'),
        tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
    ).properties(title='Monthly PM10: Gujarat, Gujarat, UP – 2019', width=550, height=320)
    return chart","<code>
 import pandas as pd
 import altair as alt
 def get_chart(data: pd.DataFrame, states_data: pd.DataFrame, ncap_funding_data: pd.DataFrame):
     states = ['Gujarat', 'Gujarat', 'Jharkhand']
     df = data[(data['state'].isin(states)) & (data['Timestamp'].dt.year == 2019)].copy()
     df['Month'] = df['Timestamp'].dt.month
     df = df.groupby(['Month','state'])['PM10'].mean().reset_index().dropna()
     chart = alt.Chart(df).mark_line(point=True).encode(
         x=alt.X('Month:O', title='Month'),
         y=alt.Y('PM10:Q', title='Average PM10 (µg/m³)'),
         color=alt.Color('state:N', title='State'),
         tooltip=['state:N','Month:O', alt.Tooltip('PM10:Q', format='.1f')]
     ).properties(title='Monthly PM10: Gujarat, Gujarat, UP – 2019', width=550, height=320)
     return chart


</code>
"